From 88cd9d3981b2daab4581f62eaac94eba2d9aeae0 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Wed, 11 Mar 2026 11:19:57 -0700 Subject: [PATCH 01/27] Add study inventory script --- pytheranostics/imaging_ds/dicom_ingest.py | 289 +++++++++++++++++++++- 1 file changed, 288 insertions(+), 1 deletion(-) diff --git a/pytheranostics/imaging_ds/dicom_ingest.py b/pytheranostics/imaging_ds/dicom_ingest.py index 48305e1..8ce1e95 100644 --- a/pytheranostics/imaging_ds/dicom_ingest.py +++ b/pytheranostics/imaging_ds/dicom_ingest.py @@ -5,10 +5,13 @@ """ import logging +import os +from datetime import datetime from pathlib import Path -from typing import Dict, List, Optional, Tuple +from typing import Any, Dict, Iterable, List, Optional, Tuple import pydicom +from pydicom.misc import is_dicom logger = logging.getLogger(__name__) @@ -282,6 +285,290 @@ def auto_setup_dosimetry_study( ) +def auto_setup_dosimetry_study_inventory( + base_dir: Path, patient_id: Optional[str] = None +) -> Tuple[Dict[str, Any], List[str], List[str], List[str]]: + """ + Build a DICOM inventory of SPECT/NM/OT/PT, CT, and RTSTRUCT series. + + Parameters + ---------- + base_dir : Path + Base directory containing DICOM files. + patient_id : str, optional + Patient ID override. If None, the first detected PatientID is used. + + Returns + ------- + tuple + (study_info, ct_paths, spect_paths, rtstruct_files) + """ + + def _iter_dicom_files(root: Path) -> Iterable[Path]: + for path in root.rglob("*"): + if not path.is_file(): + continue + if path.suffix.lower() == ".dcm" or is_dicom(str(path)): + yield path + + def _parse_datetime( + date_str: Optional[str], time_str: Optional[str] + ) -> Optional[datetime]: + if not date_str: + return None + time_val = (time_str or "000000").split(".")[0] + if len(time_val) < 6: + time_val = time_val.ljust(6, "0") + try: + return datetime.strptime(f"{date_str}{time_val}", "%Y%m%d%H%M%S") + except ValueError: + return None + + def _dicom_datetime(ds: pydicom.Dataset) -> Optional[datetime]: + for date_key, time_key in [ + ("AcquisitionDate", "AcquisitionTime"), + ("SeriesDate", "SeriesTime"), + ("ContentDate", "ContentTime"), + ("StudyDate", "StudyTime"), + ]: + date_val = getattr(ds, date_key, None) + time_val = getattr(ds, time_key, None) + dt = _parse_datetime(date_val, time_val) + if dt: + return dt + return None + + def _common_parent(files: List[Path]) -> Optional[str]: + if not files: + return None + common = Path(os.path.commonpath([str(p) for p in files])) + if common.is_file(): + return str(common.parent) + return str(common) + + def _select_best_match( + target: Dict[str, Any], candidates: List[Dict[str, Any]] + ) -> Optional[Dict[str, Any]]: + if not candidates: + return None + target_study = target.get("study_uid") + filtered = [c for c in candidates if c.get("study_uid") == target_study] + if filtered: + candidates = filtered + target_dt = target.get("series_datetime") + dated = [c for c in candidates if c.get("series_datetime")] + if target_dt and dated: + return min( + dated, + key=lambda c: abs((c["series_datetime"] - target_dt).total_seconds()), + ) + return candidates[0] + + def _extract_injection_info(ds: pydicom.Dataset) -> Dict[str, Any]: + info: Dict[str, Any] = { + "patient_weight_kg": getattr(ds, "PatientWeight", None), + "injection_date": None, + "injection_time": None, + "injected_activity": None, + "radiopharmaceutical": None, + } + + if info["patient_weight_kg"]: + info["patient_weight_g"] = int(info["patient_weight_kg"] * 1000) + + if hasattr(ds, "RadiopharmaceuticalInformationSequence"): + rp_seq = ds.RadiopharmaceuticalInformationSequence + if len(rp_seq) > 0: + rp_info = rp_seq[0] + info["radiopharmaceutical"] = getattr( + rp_info, "Radiopharmaceutical", None + ) + info["injected_activity"] = getattr( + rp_info, "RadionuclideTotalDose", None + ) + + inj_date = getattr(rp_info, "RadiopharmaceuticalStartDate", None) + inj_time = getattr(rp_info, "RadiopharmaceuticalStartTime", None) + + if inj_date: + info["injection_date"] = inj_date + if inj_time: + info["injection_time"] = inj_time.split(".")[0] + + return info + + series_map: Dict[str, Dict[str, Any]] = {} + detected_patient_id: Optional[str] = None + + tag_list = [ + "Modality", + "SeriesInstanceUID", + "StudyInstanceUID", + "StudyDate", + "StudyTime", + "SeriesDate", + "SeriesTime", + "AcquisitionDate", + "AcquisitionTime", + "ContentDate", + "ContentTime", + "PatientID", + ] + + for dcm_path in _iter_dicom_files(Path(base_dir)): + try: + ds = pydicom.dcmread( + dcm_path, stop_before_pixels=True, force=True, specific_tags=tag_list + ) + except Exception as exc: + logger.warning(f"Skipping unreadable DICOM: {dcm_path} ({exc})") + continue + + modality = getattr(ds, "Modality", None) + if modality not in {"CT", "RTSTRUCT", "NM", "OT", "PT"}: + continue + + series_uid = getattr(ds, "SeriesInstanceUID", None) + study_uid = getattr(ds, "StudyInstanceUID", None) + series_key = series_uid or f"{study_uid}|{modality}|{dcm_path.parent}" + + if series_key not in series_map: + series_map[series_key] = { + "modality": modality, + "study_uid": study_uid, + "series_uid": series_uid, + "files": [], + "series_datetime": _dicom_datetime(ds), + "patient_id": getattr(ds, "PatientID", None), + "representative_ds": ds, + } + + series_map[series_key]["files"].append(dcm_path) + + if not detected_patient_id: + detected_patient_id = getattr(ds, "PatientID", None) + + spect_series = [ + s for s in series_map.values() if s["modality"] in {"NM", "OT", "PT"} + ] + ct_series = [s for s in series_map.values() if s["modality"] == "CT"] + rt_series = [s for s in series_map.values() if s["modality"] == "RTSTRUCT"] + + logger.info( + "DICOM inventory: %d SPECT series, %d CT series, %d RTSTRUCT series", + len(spect_series), + len(ct_series), + len(rt_series), + ) + + for series in spect_series: + logger.info( + "SPECT series: study_uid=%s series_uid=%s datetime=%s files=%d root=%s", + series.get("study_uid"), + series.get("series_uid"), + series.get("series_datetime"), + len(series.get("files", [])), + _common_parent(series.get("files", [])) or "UNKNOWN", + ) + + for series in ct_series: + logger.info( + "CT series: study_uid=%s series_uid=%s datetime=%s files=%d root=%s", + series.get("study_uid"), + series.get("series_uid"), + series.get("series_datetime"), + len(series.get("files", [])), + _common_parent(series.get("files", [])) or "UNKNOWN", + ) + + for series in rt_series: + logger.info( + "RTSTRUCT series: study_uid=%s series_uid=%s datetime=%s files=%d root=%s", + series.get("study_uid"), + series.get("series_uid"), + series.get("series_datetime"), + len(series.get("files", [])), + _common_parent(series.get("files", [])) or "UNKNOWN", + ) + + time_points: List[Dict[str, Any]] = [] + ct_paths: List[str] = [] + spect_paths: List[str] = [] + rtstruct_files: List[str] = [] + + def _spect_sort_key(series: Dict[str, Any]) -> Tuple[int, datetime]: + dt_val = series.get("series_datetime") + if dt_val: + return (0, dt_val) + return (1, datetime.max) + + for idx, spect in enumerate(sorted(spect_series, key=_spect_sort_key)): + ct_match = _select_best_match(spect, ct_series) + rt_match = _select_best_match(spect, rt_series) + + spect_path = _common_parent(spect["files"]) + ct_path = _common_parent(ct_match["files"]) if ct_match else None + rt_file = str(rt_match["files"][0]) if rt_match and rt_match["files"] else None + + logger.info( + "Time point %s: SPECT=%s CT=%s RTSTRUCT=%s", + f"tp{idx + 1}", + spect_path or "NONE", + ct_path or "NONE", + rt_file or "NONE", + ) + if ct_match: + logger.info( + "Time point %s CT match: study_uid=%s series_uid=%s datetime=%s", + f"tp{idx + 1}", + ct_match.get("study_uid"), + ct_match.get("series_uid"), + ct_match.get("series_datetime"), + ) + if rt_match: + logger.info( + "Time point %s RTSTRUCT match: study_uid=%s series_uid=%s datetime=%s", + f"tp{idx + 1}", + rt_match.get("study_uid"), + rt_match.get("series_uid"), + rt_match.get("series_datetime"), + ) + + tp_info: Dict[str, Any] = { + "name": f"tp{idx + 1}", + "spect_path": spect_path, + "ct_path": ct_path, + "rtstruct_file": rt_file, + "series_datetime": spect.get("series_datetime"), + "study_uid": spect.get("study_uid"), + "series_uid": spect.get("series_uid"), + "patient_id": spect.get("patient_id"), + "injection_info": ( + _extract_injection_info(spect["representative_ds"]) + if spect.get("representative_ds") + else {} + ), + } + + time_points.append(tp_info) + if ct_path: + ct_paths.append(ct_path) + if spect_path: + spect_paths.append(spect_path) + if rt_file: + rtstruct_files.append(rt_file) + + study_info: Dict[str, Any] = { + "patient_id": patient_id or detected_patient_id, + "time_points": time_points, + "ct_paths": ct_paths, + "spect_paths": spect_paths, + "rtstruct_files": rtstruct_files, + } + + return study_info, ct_paths, spect_paths, rtstruct_files + + def extract_patient_metadata(dicom_dir: Path) -> Dict: """ Extract patient metadata from the first DICOM file in a directory. From 0e7f8ae50843393418490a5189ef9fc286d11260 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Wed, 11 Mar 2026 11:20:31 -0700 Subject: [PATCH 02/27] Fix comment --- .../tutorials/segmentation/total_segmentator_tutorial.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/tutorials/segmentation/total_segmentator_tutorial.ipynb b/docs/source/tutorials/segmentation/total_segmentator_tutorial.ipynb index 71e46e2..9d4b055 100644 --- a/docs/source/tutorials/segmentation/total_segmentator_tutorial.ipynb +++ b/docs/source/tutorials/segmentation/total_segmentator_tutorial.ipynb @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "fce08eb7", "metadata": {}, "outputs": [ @@ -313,7 +313,7 @@ "seg_result = totalseg_segment(\n", " root_dir=str(project_data_dir),\n", " base_output_dir=str(totalsegmentator_output_dir),\n", - " device=\"mps\", # Change to \"cuda\" if using an NVIDIA GPU, or \"cpu\" to run on CPU\n", + " device=\"mps\", # Change to \"gpu\" if using an NVIDIA GPU, or \"cpu\" to run on CPU\n", " parallel=True,\n", " max_workers=4, # Number of parallel workers to use if parallel=True\n", ")" @@ -515,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d5192b60", "metadata": {}, "outputs": [ From 98a109365bfceb1b548dc5885969f915f0c6786d Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Wed, 11 Mar 2026 11:21:04 -0700 Subject: [PATCH 03/27] Update imports on Data Ingestion Tutorial --- .../Data_Ingestion_Examples.ipynb | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/source/tutorials/Data_Ingestion_Examples/Data_Ingestion_Examples.ipynb b/docs/source/tutorials/Data_Ingestion_Examples/Data_Ingestion_Examples.ipynb index 6e691f8..ad83d00 100644 --- a/docs/source/tutorials/Data_Ingestion_Examples/Data_Ingestion_Examples.ipynb +++ b/docs/source/tutorials/Data_Ingestion_Examples/Data_Ingestion_Examples.ipynb @@ -25,7 +25,7 @@ "# Imports\n", "from pathlib import Path\n", "import pytheranostics as tx\n", - "from pytheranostics.ImagingDS.dicom_ingest import auto_setup_dosimetry_study, extract_patient_metadata\n", + "from pytheranostics.imaging_ds.dicom_ingest import auto_setup_dosimetry_study, extract_patient_metadata\n", "\n", "print('PyTheranostics version:', getattr(tx, '__version__', 'unknown'))" ] @@ -201,8 +201,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": ".pyThera-DBG", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.12" } }, "nbformat": 4, From a8d526869b8a083e2fa793841a867c6d9888a3cf Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Thu, 12 Mar 2026 13:08:56 -0700 Subject: [PATCH 04/27] Updated cycle_loader with inventory support --- pytheranostics/imaging_ds/cycle_loader.py | 113 +++++++++++++++++++--- 1 file changed, 100 insertions(+), 13 deletions(-) diff --git a/pytheranostics/imaging_ds/cycle_loader.py b/pytheranostics/imaging_ds/cycle_loader.py index 0dd7013..17a2d9c 100644 --- a/pytheranostics/imaging_ds/cycle_loader.py +++ b/pytheranostics/imaging_ds/cycle_loader.py @@ -10,7 +10,7 @@ import json import re from pathlib import Path -from typing import Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import pydicom import SimpleITK @@ -300,10 +300,11 @@ def _build_auto_mapping(mask_keys: List[str]) -> Dict[str, str]: def create_studies_with_masks( - storage_root: str | Path, - patient_id: str, - cycle_no: int, + storage_root: Optional[str | Path] = None, + patient_id: Optional[str] = None, + cycle_no: Optional[int] = None, *, + study_info: Optional[Dict[str, Any]] = None, calibration_factor: Optional[float] = None, parallel: bool = True, max_workers: Optional[int] = None, @@ -327,12 +328,18 @@ def create_studies_with_masks( Parameters ---------- - storage_root : str | Path - Base directory where organized data lives. - patient_id : str - Patient identifier. - cycle_no : int - Cycle number (1-based). + storage_root : str | Path, optional + Base directory where organized data lives. Used with patient_id + cycle_no + for the legacy folder-structured loading behavior. + patient_id : str, optional + Patient identifier used with storage_root. + cycle_no : int, optional + Cycle number (1-based), used with storage_root. + study_info : dict, optional + Study dictionary returned by auto_setup_dosimetry_study() or + auto_setup_dosimetry_study_inventory(). If provided, CT/SPECT/RTSTRUCT + paths are loaded from this dictionary and storage_root/patient_id/cycle_no + are not required. calibration_factor : float, optional Optional SPECT calibration factor (Bq per count/sec) applied during image load. parallel : bool @@ -385,9 +392,89 @@ def create_studies_with_masks( ) # 1) Discover paths and injection metadata - ct_paths, spect_paths, rtstruct_files, inj = prepare_cycle_inputs( - storage_root, patient_id, cycle_no - ) + if study_info is not None: + tp_items = study_info.get("time_points", []) + if tp_items: + ct_paths = [ + Path(tp["ct_path"]) if tp.get("ct_path") else None for tp in tp_items + ] + spect_paths = [ + Path(tp["spect_path"]) if tp.get("spect_path") else None + for tp in tp_items + ] + rtstruct_files = [ + Path(tp["rtstruct_file"]) if tp.get("rtstruct_file") else None + for tp in tp_items + ] + first_tp_with_inj = next( + ( + tp + for tp in tp_items + if isinstance(tp.get("injection_info"), dict) + and tp.get("injection_info") + ), + None, + ) + inj_info = ( + first_tp_with_inj.get("injection_info", {}) + if first_tp_with_inj is not None + else {} + ) + else: + ct_paths = [Path(p) if p else None for p in study_info.get("ct_paths", [])] + spect_paths = [ + Path(p) if p else None for p in study_info.get("spect_paths", []) + ] + rtstruct_files = [ + Path(p) if p else None for p in study_info.get("rtstruct_files", []) + ] + inj_info = {} + + max_len = max(len(ct_paths), len(spect_paths), len(rtstruct_files), 0) + ct_paths.extend([None] * (max_len - len(ct_paths))) + spect_paths.extend([None] * (max_len - len(spect_paths))) + rtstruct_files.extend([None] * (max_len - len(rtstruct_files))) + + weight_g = inj_info.get("PatientWeight_g") + if weight_g is None: + weight_g = inj_info.get("patient_weight_g") + if weight_g is None and inj_info.get("patient_weight_kg") is not None: + try: + weight_g = int(round(float(inj_info["patient_weight_kg"]) * 1000.0)) + except Exception: + weight_g = None + + injected_activity = ( + inj_info.get("InjectedActivity") + if "InjectedActivity" in inj_info + else inj_info.get("injected_activity") + ) + if injected_activity is not None: + try: + injected_activity = int(round(float(injected_activity))) + except Exception: + pass + + inj = { + "InjectionDate": inj_info.get("InjectionDate") + or inj_info.get("injection_date") + or "", + "InjectionTime": _extract_hhmmss( + inj_info.get("InjectionTime") or inj_info.get("injection_time") + ) + or "", + "InjectedActivity": injected_activity, + "PatientWeight_g": weight_g, + "PatientHeight_cm": inj_info.get("PatientHeight_cm"), + } + else: + if storage_root is None or patient_id is None or cycle_no is None: + raise ValueError( + "Either provide study_info, or provide storage_root + patient_id + cycle_no." + ) + ct_paths, spect_paths, rtstruct_files, inj = prepare_cycle_inputs( + storage_root, patient_id, cycle_no + ) # 2) Build longitudinal studies from DICOM once ct_dirs = [str(p) for p in ct_paths if p is not None] From 2cbe0c291aebc934d519d4974bd3ddc92a696b8f Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Thu, 12 Mar 2026 14:03:50 -0700 Subject: [PATCH 05/27] Fix date time --- pytheranostics/imaging_ds/dicom_ingest.py | 124 ++++++++++++++++++---- 1 file changed, 101 insertions(+), 23 deletions(-) diff --git a/pytheranostics/imaging_ds/dicom_ingest.py b/pytheranostics/imaging_ds/dicom_ingest.py index 8ce1e95..5a25861 100644 --- a/pytheranostics/imaging_ds/dicom_ingest.py +++ b/pytheranostics/imaging_ds/dicom_ingest.py @@ -16,6 +16,22 @@ logger = logging.getLogger(__name__) +def _split_dicom_datetime(dt_val: Optional[str]) -> Tuple[Optional[str], Optional[str]]: + """ + Split a DICOM DT value into date (YYYYMMDD) and time (HHMMSS). + """ + if not dt_val: + return None, None + val = str(dt_val).split(".")[0] + if len(val) < 8: + return None, None + date_part = val[:8] + time_part = val[8:14] if len(val) > 8 else None + if time_part: + time_part = time_part.ljust(6, "0") + return date_part, time_part + + class DosimetryStudyOrganizer: """ Organize DICOM studies for dosimetry analysis. @@ -181,17 +197,26 @@ def _extract_injection_info(self, ds: pydicom.Dataset) -> Dict: rp_info, "RadionuclideTotalDose", None ) - # Extract date and time - inj_date = getattr(rp_info, "RadiopharmaceuticalStartDate", None) - inj_time = getattr(rp_info, "RadiopharmaceuticalStartTime", None) - + inj_dt = getattr(rp_info, "RadiopharmaceuticalStartDateTime", None) + inj_date, inj_time = _split_dicom_datetime(inj_dt) if inj_date: info["injection_date"] = inj_date if inj_time: - # Format time to HHMMSS - info["injection_time"] = inj_time.split(".")[ - 0 - ] # Remove fractional seconds + info["injection_time"] = inj_time + + # Backward-compatible fallback when DateTime is not available. + if not info["injection_date"]: + inj_date_legacy = getattr( + rp_info, "RadiopharmaceuticalStartDate", None + ) + if inj_date_legacy: + info["injection_date"] = inj_date_legacy + if not info["injection_time"]: + inj_time_legacy = getattr( + rp_info, "RadiopharmaceuticalStartTime", None + ) + if inj_time_legacy: + info["injection_time"] = inj_time_legacy.split(".")[0] return info @@ -289,7 +314,7 @@ def auto_setup_dosimetry_study_inventory( base_dir: Path, patient_id: Optional[str] = None ) -> Tuple[Dict[str, Any], List[str], List[str], List[str]]: """ - Build a DICOM inventory of SPECT/NM/OT/PT, CT, and RTSTRUCT series. + Build a DICOM inventory of SPECT (NM/OT/PT), CT, and RTSTRUCT series. Parameters ---------- @@ -387,13 +412,26 @@ def _extract_injection_info(ds: pydicom.Dataset) -> Dict[str, Any]: rp_info, "RadionuclideTotalDose", None ) - inj_date = getattr(rp_info, "RadiopharmaceuticalStartDate", None) - inj_time = getattr(rp_info, "RadiopharmaceuticalStartTime", None) - + inj_dt = getattr(rp_info, "RadiopharmaceuticalStartDateTime", None) + inj_date, inj_time = _split_dicom_datetime(inj_dt) if inj_date: info["injection_date"] = inj_date if inj_time: - info["injection_time"] = inj_time.split(".")[0] + info["injection_time"] = inj_time + + # Backward-compatible fallback when DateTime is not available. + if not info["injection_date"]: + inj_date_legacy = getattr( + rp_info, "RadiopharmaceuticalStartDate", None + ) + if inj_date_legacy: + info["injection_date"] = inj_date_legacy + if not info["injection_time"]: + inj_time_legacy = getattr( + rp_info, "RadiopharmaceuticalStartTime", None + ) + if inj_time_legacy: + info["injection_time"] = inj_time_legacy.split(".")[0] return info @@ -401,9 +439,12 @@ def _extract_injection_info(ds: pydicom.Dataset) -> Dict[str, Any]: detected_patient_id: Optional[str] = None tag_list = [ + # Core indexing/matching fields "Modality", "SeriesInstanceUID", "StudyInstanceUID", + "SOPClassUID", + "SOPInstanceUID", "StudyDate", "StudyTime", "SeriesDate", @@ -413,6 +454,38 @@ def _extract_injection_info(ds: pydicom.Dataset) -> Dict[str, Any]: "ContentDate", "ContentTime", "PatientID", + "PatientName", + "PatientSex", + "PatientBirthDate", + "PatientWeight", + # Common geometric/acquisition fields + "FrameOfReferenceUID", + "ImagePositionPatient", + "ImageOrientationPatient", + "PixelSpacing", + "SliceThickness", + "KVP", + "ConvolutionKernel", + "RescaleIntercept", + "RescaleSlope", + # NM/PT quantitative + injection fields + "SeriesDescription", + "AcquisitionDuration", + "Units", + "DecayCorrection", + "CorrectedImage", + "CountsSource", + "NumberOfSlices", + "RadiopharmaceuticalInformationSequence", + "RadiopharmaceuticalStartDateTime", + # RTSTRUCT linkage/content fields + "StructureSetLabel", + "StructureSetDate", + "StructureSetTime", + "ReferencedFrameOfReferenceSequence", + "StructureSetROISequence", + "ROIContourSequence", + "RTROIObservationsSequence", ] for dcm_path in _iter_dicom_files(Path(base_dir)): @@ -607,15 +680,20 @@ def extract_patient_metadata(dicom_dir: Path) -> Dict: metadata["injected_activity"] = getattr( rp_info, "RadionuclideTotalDose", None ) - metadata["injection_date"] = getattr( - rp_info, "RadiopharmaceuticalStartDate", None - ) - metadata["injection_time"] = getattr( - rp_info, "RadiopharmaceuticalStartTime", None - ) - if metadata["injection_time"]: - metadata["injection_time"] = metadata["injection_time"].split(".")[ - 0 - ] + inj_dt = getattr(rp_info, "RadiopharmaceuticalStartDateTime", None) + inj_date, inj_time = _split_dicom_datetime(inj_dt) + metadata["injection_date"] = inj_date + metadata["injection_time"] = inj_time + + if not metadata["injection_date"]: + metadata["injection_date"] = getattr( + rp_info, "RadiopharmaceuticalStartDate", None + ) + if not metadata["injection_time"]: + inj_time_legacy = getattr( + rp_info, "RadiopharmaceuticalStartTime", None + ) + if inj_time_legacy: + metadata["injection_time"] = inj_time_legacy.split(".")[0] return metadata From ab0bc9e4e4cf65e56ef49dbf8fcf17e890533ab8 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Thu, 19 Mar 2026 14:03:18 -0700 Subject: [PATCH 06/27] add check for SPECT availability --- pytheranostics/imaging_ds/cycle_loader.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pytheranostics/imaging_ds/cycle_loader.py b/pytheranostics/imaging_ds/cycle_loader.py index 17a2d9c..e094162 100644 --- a/pytheranostics/imaging_ds/cycle_loader.py +++ b/pytheranostics/imaging_ds/cycle_loader.py @@ -509,6 +509,11 @@ def create_studies_with_masks( # Build NM masks only if target available target_img = next(iter(longSPECT.images.values()), None) + if target_img is None: + raise RuntimeError( + f"No SPECT image available for timepoint {time_id} to resample NM masks" + ) + ct_masks, nm_masks = load_and_resample_RT_to_target( ref_dicom_ct_dir=str(ct_dir), rt_struct_file=str(rt_file), @@ -570,6 +575,7 @@ def _is_valid_target(name: str) -> bool: if final_spect_mapping is not None: for k in nm_masks.keys(): dst = final_spect_mapping.get(k) + if dst is not None and _is_valid_target(dst): spect_map_valid[k] = dst else: From a1f94832c6900f63b45655432880b03c8f3d33b7 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Thu, 19 Mar 2026 14:33:10 -0700 Subject: [PATCH 07/27] Set config values to default if not-available --- pytheranostics/dosimetry/base_dosimetry.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pytheranostics/dosimetry/base_dosimetry.py b/pytheranostics/dosimetry/base_dosimetry.py index 8af3cd5..d728ba7 100644 --- a/pytheranostics/dosimetry/base_dosimetry.py +++ b/pytheranostics/dosimetry/base_dosimetry.py @@ -87,9 +87,10 @@ def __init__( self.toMBq = 1e-6 # Factor to scale activity from Bq to MBq # Store data - self.patient_id = config["PatientID"] - self.cycle = config["Cycle"] - self.db_dir = Path(config["DatabaseDir"]) + self.patient_id = config["PatientID"] if "PatientID" in config else "UnknownPatient" + self.cycle = config["Cycle"] if "Cycle" in config else 1 + self.db_dir = Path(config["DatabaseDir"]) if "DatabaseDir" in config else Path("./") + self.check_mandatory_fields() self.check_patient_in_db() # TODO: Traceability/database? From 7e35c9c4ad268520cbf6796f3d5a4769b607796f Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Thu, 19 Mar 2026 16:57:55 -0700 Subject: [PATCH 08/27] Fix a few bugs with data loading and missing whole-body and remainderofbody --- pytheranostics/dosimetry/base_dosimetry.py | 25 ++++++++++++++++--- pytheranostics/dosimetry/config.py | 6 +++++ pytheranostics/dosimetry/voxel_s_dosimetry.py | 6 ++++- 3 files changed, 33 insertions(+), 4 deletions(-) diff --git a/pytheranostics/dosimetry/base_dosimetry.py b/pytheranostics/dosimetry/base_dosimetry.py index d728ba7..cf056c0 100644 --- a/pytheranostics/dosimetry/base_dosimetry.py +++ b/pytheranostics/dosimetry/base_dosimetry.py @@ -232,6 +232,21 @@ def check_mandatory_fields(self) -> None: print("No Reference Time point was given. Assigning time ID = 0") self.config["ReferenceTimePoint"] = 0 + # If WholeBody and RemainderOfBody were not defined by the user, add them by default to the VOIs to ensure consistency with dosimetry calculations. + for missing in ["WholeBody", "RemainderOfBody"]: + if missing not in self.config["VOIs"]: + print( + f"Adding {missing} to the list of VOIs with default parameters. This region is required for dosimetry calculations." + ) + self.config["VOIs"][missing] = { + "fit_order": None, + "with_uptake": None, + "fixed_parameters": None, + "bounds": None, + "param_init": None, + } + + if "Organ" in self.config["Level"]: if "WholeBody" not in self.config["VOIs"]: if "No" in self.config["OrganLevel"]["AdditionalOptions"]["WholeBody"]: @@ -255,7 +270,7 @@ def initialize(self) -> pandas.DataFrame: tmp_results: Dict[str, List[float]] = { roi_name: [] for roi_name in self.nm_data.masks[0].keys() - if roi_name in self.config["VOIs"] + if roi_name in self.config["VOIs"] or roi_name in ["WholeBody", "RemainderOfBody"] } cols: List[str] = ["Time_hr", "Volume_CT_mL", "Activity_MBq", "Density_HU"] @@ -266,7 +281,7 @@ def initialize(self) -> pandas.DataFrame: self.normalize_time_to_injection(time_id=time_id) for roi_name in tmp_results.keys(): - + # Time (relative to time of injection, in hours) tmp_results[roi_name].append( [self.nm_data.meta[time_id].HoursAfterInjection for time_id in time_ids] @@ -412,6 +427,10 @@ def compute_tia(self) -> None: } for region, region_data in self.results.iterrows(): + + if type(region) != str: + raise TypeError(f"Region names should be strings. Found {type(region)} instead.") + fit_results = self.smart_fit_selection( region_data=region_data, region=region ) @@ -419,7 +438,7 @@ def compute_tia(self) -> None: plot_tac_residuals( result=fit_results, region=region, - cycle=self.config["Cycle"], + cycle=self.cycle, output_dir=self.db_dir, ) diff --git a/pytheranostics/dosimetry/config.py b/pytheranostics/dosimetry/config.py index 8902658..b2d0442 100644 --- a/pytheranostics/dosimetry/config.py +++ b/pytheranostics/dosimetry/config.py @@ -158,6 +158,12 @@ def build_roi_fit_config( all_masks = set() for tp_masks in getattr(longSPECT, "masks", {}).values(): all_masks.update(tp_masks.keys()) + + # Add WholeBody and RemainderOfBody if they are not available in masks, as they will be needed for dosimetry (and will be generated by default if not present) + if "WholeBody" not in all_masks: + all_masks.add("WholeBody") + if "RemainderOfBody" not in all_masks: + all_masks.add("RemainderOfBody") # Get valid organ names from config try: diff --git a/pytheranostics/dosimetry/voxel_s_dosimetry.py b/pytheranostics/dosimetry/voxel_s_dosimetry.py index 3dd719e..16d929d 100644 --- a/pytheranostics/dosimetry/voxel_s_dosimetry.py +++ b/pytheranostics/dosimetry/voxel_s_dosimetry.py @@ -70,6 +70,10 @@ def compute_voxel_tia(self) -> None: masks = numpy.zeros_like(tia_map, dtype=numpy.int8) for region, region_data in self.results.iterrows(): + + # Type check: + if type(region) != str: + raise TypeError(f"Region names should be strings. Found {type(region)} instead.") if region == "WholeBody": continue # We do not want to double count voxels! @@ -90,7 +94,7 @@ def compute_voxel_tia(self) -> None: region_tia = region_data["TIA_MBq_h"] region_fit_params = region_data["Fit_params"] # fit params - exp_order = self.config["rois"][region]["fit_order"] + exp_order = self.config["VOIs"][region]["fit_order"] region_fit, _, _ = get_exponential( order=exp_order, param_init=None, decayconst=1.0 ) # Decay-constant not used here. From 2a7a429b4a63ef62934ee5f8aacee1b9917c8c93 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Sat, 21 Mar 2026 21:05:45 -0700 Subject: [PATCH 09/27] Update dvk to read Graves et al. kernels --- pytheranostics/dosimetry/dvk.py | 326 +++++++++++++++++++++++++++++--- 1 file changed, 304 insertions(+), 22 deletions(-) diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index 96822f8..6498f4f 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -1,43 +1,325 @@ """Dose voxel kernel module for convolution-based dosimetry.""" -from typing import Optional +from __future__ import annotations + +import csv +import logging +import re +import shutil +import tempfile +import urllib.error +import urllib.request +import zipfile +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional, Sequence, Tuple import numpy from scipy import signal from pytheranostics.misc_tools.tools import hu_to_rho -from pytheranostics.shared.resources import resource_path + +logger = logging.getLogger(__name__) + +_DATA_DIR = Path(__file__).resolve().parent.parent / "data" / "voxel_kernels" +_ZENODO_RECORD_ID = "7596345" +_DEFAULT_CROP_SIZE_MM = 25.0 +_CSV_PATTERN = re.compile( + r"^(?P[0-9]+[A-Za-z]+)" + r"_(?P[^_]+)" + r"_(?P[^_]+)" + r"_(?P[0-9]+(?:\.[0-9]+)?)mm" + r"_(?P\d+)x(?P=matrix_size)x(?P=matrix_size)\.csv$", + flags=re.IGNORECASE, +) + + +@dataclass(frozen=True) +class KernelMetadata: + """Metadata parsed from a dose kernel CSV filename.""" + + path: Path + isotope: str + voxel_size_mm: float + matrix_size: int + + +def _split_isotope(isotope: str) -> Tuple[str, str]: + """Return isotope mass number and symbol from either `Lu177` or `177Lu`.""" + match = re.fullmatch(r"(?P[A-Za-z]+)(?P\d+)", isotope) + if match is None: + match = re.fullmatch(r"(?P\d+)(?P[A-Za-z]+)", isotope) + + if match is None: + raise ValueError( + "Isotope must be formatted like 'Lu177' or '177Lu'. " + f"Received '{isotope}'." + ) + + return match.group("mass"), match.group("symbol") + + +def _canonical_isotope(isotope: str) -> str: + """Convert an isotope string to a canonical representation for comparisons.""" + mass, symbol = _split_isotope(isotope) + return f"{symbol.capitalize()}{mass}" + + +def _zenodo_isotope_name(isotope: str) -> str: + """Convert an isotope string to Zenodo's archive naming convention.""" + mass, symbol = _split_isotope(isotope) + return f"{mass}{symbol.capitalize()}" + + +def _discover_kernel_files(kernel_dir: Path, isotope: str) -> List[KernelMetadata]: + """Return kernel CSVs matching the requested isotope.""" + requested_isotope = _canonical_isotope(isotope) + kernels: List[KernelMetadata] = [] + + for path in sorted(kernel_dir.rglob("*.csv")): + match = _CSV_PATTERN.match(path.name) + if match is None: + continue + + parsed_isotope = _canonical_isotope(match.group("isotope")) + if parsed_isotope != requested_isotope: + continue + + kernels.append( + KernelMetadata( + path=path, + isotope=parsed_isotope, + voxel_size_mm=float(match.group("voxel_size")), + matrix_size=int(match.group("matrix_size")), + ) + ) + + return kernels + + +def _select_closest_kernel( + requested_voxel_size_mm: float, available_kernels: Sequence[KernelMetadata] +) -> KernelMetadata: + """Return the kernel whose voxel size is closest to the requested size.""" + if not available_kernels: + raise ValueError("No kernels were provided for voxel-size selection.") + + closest_kernel = min( + available_kernels, + key=lambda kernel: abs(kernel.voxel_size_mm - requested_voxel_size_mm), + ) + delta_mm = abs(closest_kernel.voxel_size_mm - requested_voxel_size_mm) + + if delta_mm > 0.09: + logger.warning( + "Requested voxel size %.3f mm does not closely match an available " + "kernel. Using %.4f mm instead.", + requested_voxel_size_mm, + closest_kernel.voxel_size_mm, + ) + + return closest_kernel + + +def _download_kernels_from_zenodo(isotope: str, kernel_dir: Path) -> None: + """Download and extract dose-kernel CSVs for the requested isotope.""" + zenodo_name = _zenodo_isotope_name(isotope) + url = ( + f"https://zenodo.org/records/{_ZENODO_RECORD_ID}/files/" + f"{zenodo_name}.zip?download=1" + ) + + kernel_dir.mkdir(parents=True, exist_ok=True) + + try: + with urllib.request.urlopen(url, timeout=120) as response: + with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as tmp_file: + shutil.copyfileobj(response, tmp_file) + archive_path = Path(tmp_file.name) + except urllib.error.HTTPError as exc: + if exc.code == 404: + logger.error( + "No dose voxel kernel archive is available on Zenodo for isotope %s.", + isotope, + ) + raise NotImplementedError( + f"Dose voxel kernels for isotope '{isotope}' are not available on Zenodo." + ) from exc + raise + + try: + with zipfile.ZipFile(archive_path) as archive: + csv_members = [ + member for member in archive.infolist() if member.filename.lower().endswith(".csv") + ] + if not csv_members: + raise FileNotFoundError( + f"Downloaded archive for isotope '{isotope}' does not contain CSV kernels." + ) + + for member in csv_members: + archive.extract(member, path=kernel_dir) + finally: + archive_path.unlink(missing_ok=True) + + logger.info("Downloaded dose voxel kernels for %s from %s", isotope, url) + + +def _ensure_kernel_files_available(kernel_dir: Path, isotope: str) -> List[KernelMetadata]: + """Ensure that kernel CSVs for the requested isotope are available locally.""" + kernels = _discover_kernel_files(kernel_dir=kernel_dir, isotope=isotope) + if kernels: + return kernels + + has_any_csv_kernels = any( + _CSV_PATTERN.match(path.name) is not None for path in kernel_dir.rglob("*.csv") + ) + if not has_any_csv_kernels: + logger.info("No kernel CSV files found under %s. Downloading %s kernels.", kernel_dir, isotope) + else: + logger.info( + "No local kernel CSVs found for %s under %s. Downloading isotope archive.", + isotope, + kernel_dir, + ) + + _download_kernels_from_zenodo(isotope=isotope, kernel_dir=kernel_dir) + kernels = _discover_kernel_files(kernel_dir=kernel_dir, isotope=isotope) + if not kernels: + raise NotImplementedError( + f"Dose voxel kernels for isotope '{isotope}' were not found after download." + ) + + return kernels + + +def _load_octant_kernel(csv_path: Path) -> numpy.ndarray: + """Load the positive octant stored in a kernel CSV.""" + with csv_path.open("r", encoding="utf-8", newline="") as file_obj: + rows = [row for row in csv.reader(file_obj) if any(cell.strip() for cell in row)] + + if not rows: + raise ValueError(f"Kernel CSV '{csv_path}' is empty.") + + section_size = len(rows[0]) + if section_size == 0: + raise ValueError(f"Kernel CSV '{csv_path}' does not contain numeric data.") + + if any(len(row) != section_size for row in rows): + raise ValueError(f"Kernel CSV '{csv_path}' has inconsistent row lengths.") + + if len(rows) % section_size != 0: + raise ValueError( + f"Kernel CSV '{csv_path}' cannot be reshaped into stacked square sections." + ) + + num_sections = len(rows) // section_size + octant = numpy.empty((section_size, section_size, num_sections), dtype=numpy.float64) + + for section_idx in range(num_sections): + start = section_idx * section_size + stop = start + section_size + block = numpy.asarray(rows[start:stop], dtype=numpy.float64) + octant[:, :, section_idx] = block.T + + return octant + + +def _mirror_axis_from_center(half_kernel: numpy.ndarray, axis: int) -> numpy.ndarray: + """Mirror a kernel axis where index 0 is the center voxel.""" + slicer = [slice(None)] * half_kernel.ndim + slicer[axis] = slice(1, None) + positive_side = half_kernel[tuple(slicer)] + mirrored_side = numpy.flip(positive_side, axis=axis) + return numpy.concatenate((mirrored_side, half_kernel), axis=axis) + + +def _build_full_kernel_from_octant(octant: numpy.ndarray) -> numpy.ndarray: + """Expand the stored positive octant into a full 3-D kernel.""" + kernel = octant + for axis in range(3): + kernel = _mirror_axis_from_center(kernel, axis=axis) + return kernel + + +def _odd_voxel_count_for_physical_size(size_mm: float, voxel_size_mm: float) -> int: + """Return the odd voxel count closest to a requested physical crop size.""" + if size_mm <= 0: + raise ValueError("Crop size must be positive when cropping is enabled.") + + target_voxels = max(1.0, size_mm / voxel_size_mm) + rounded_voxels = max(1, int(round(target_voxels))) + if rounded_voxels % 2 == 1: + return rounded_voxels + + lower = max(1, rounded_voxels - 1) + upper = rounded_voxels + 1 + if abs(lower - target_voxels) <= abs(upper - target_voxels): + return lower + return upper + + +def _crop_kernel_around_center( + kernel: numpy.ndarray, voxel_size_mm: float, crop_size_mm: Optional[float] +) -> numpy.ndarray: + """Crop a kernel symmetrically around its center voxel.""" + if crop_size_mm is None: + return kernel + + target_voxels = _odd_voxel_count_for_physical_size(crop_size_mm, voxel_size_mm) + if target_voxels >= kernel.shape[0]: + return kernel + + center = kernel.shape[0] // 2 + half_width = target_voxels // 2 + start = center - half_width + stop = center + half_width + 1 + return kernel[start:stop, start:stop, start:stop] class DoseVoxelKernel: """Dose Voxel Kernel for convolution-based dosimetry calculations.""" - def __init__(self, isotope: str, voxel_size_mm: float) -> None: + def __init__( + self, + isotope: str, + voxel_size_mm: float, + crop_kernel_size_mm: Optional[float] = _DEFAULT_CROP_SIZE_MM, + ) -> None: """Initialize the DoseVoxelKernel. Args ---- isotope (str): The isotope name (e.g., 'Lu177'). - voxel_size_mm (float): Voxel size in millimeters. + voxel_size_mm (float): Requested voxel size in millimeters. + crop_kernel_size_mm (float | None): Physical cubic crop size in mm. + Use ``None`` to disable cropping. """ - kernel_filename = ( - f"voxel_kernels/{isotope}-{voxel_size_mm:1.2f}-mm-mGyperMBqs-SoftICRP.img" + available_kernels = _ensure_kernel_files_available(_DATA_DIR, isotope) + selected_kernel = _select_closest_kernel( + requested_voxel_size_mm=voxel_size_mm, + available_kernels=available_kernels, ) - try: - with resource_path("pytheranostics.data", kernel_filename) as kernel_path: - self.kernel = numpy.fromfile(kernel_path, dtype=numpy.float32) - except FileNotFoundError: - print( - f" >> Voxel Kernel for SPECT voxel size ({voxel_size_mm:2.2f} mm) not found. Using default kernel for 4.8 mm voxels..." - ) - fallback_filename = ( - f"voxel_kernels/{isotope}-4.80-mm-mGyperMBqs-SoftICRP.img" + octant = _load_octant_kernel(selected_kernel.path) + full_kernel = _build_full_kernel_from_octant(octant) + + expected_shape = ( + selected_kernel.matrix_size, + selected_kernel.matrix_size, + selected_kernel.matrix_size, + ) + if full_kernel.shape != expected_shape: + raise ValueError( + "Kernel shape reconstructed from CSV does not match the filename " + f"metadata for '{selected_kernel.path.name}'." ) - with resource_path("pytheranostics.data", fallback_filename) as kernel_path: - self.kernel = numpy.fromfile(kernel_path, dtype=numpy.float32) - self.kernel = self.kernel.reshape((51, 51, 51)).astype(numpy.float64) + self.kernel = _crop_kernel_around_center( + kernel=full_kernel, + voxel_size_mm=selected_kernel.voxel_size_mm, + crop_size_mm=crop_kernel_size_mm, + ).astype(numpy.float64) def tia_to_dose( self, tia_mbq_s: numpy.ndarray, ct: Optional[numpy.ndarray] = None @@ -59,10 +341,10 @@ def tia_to_dose( dose_mGy = signal.fftconvolve(tia_mbq_s, self.kernel, mode="same", axes=None) if ct is not None: - # TODO: Handle erroneous scale-up of dose outside of body. - print( - "Warning -> Scaling dose by density will yield erroneous dose values in very low density voxels (e.g., air inside the body)." - " Please use at your own risk" + logger.warning( + "Scaling dose by density will yield erroneous dose values in very " + "low-density voxels (for example air inside the body). Please use " + "at your own risk." ) dose_mGy = self.weight_dose_by_density(dose_map=dose_mGy, ct=ct) From a850b82713ac5d7ae54125a6e914950c3439d06c Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 24 Mar 2026 10:33:51 -0700 Subject: [PATCH 10/27] Update dvk to read Graves et al. kernels --- pytheranostics/dosimetry/dvk.py | 22 ++++++-- tests/test_dvk.py | 93 +++++++++++++++++++++++++++++++++ tests/test_resource_loading.py | 10 ---- 3 files changed, 110 insertions(+), 15 deletions(-) create mode 100644 tests/test_dvk.py diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index 6498f4f..eb2eb53 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -150,7 +150,9 @@ def _download_kernels_from_zenodo(isotope: str, kernel_dir: Path) -> None: try: with zipfile.ZipFile(archive_path) as archive: csv_members = [ - member for member in archive.infolist() if member.filename.lower().endswith(".csv") + member + for member in archive.infolist() + if member.filename.lower().endswith(".csv") ] if not csv_members: raise FileNotFoundError( @@ -165,7 +167,9 @@ def _download_kernels_from_zenodo(isotope: str, kernel_dir: Path) -> None: logger.info("Downloaded dose voxel kernels for %s from %s", isotope, url) -def _ensure_kernel_files_available(kernel_dir: Path, isotope: str) -> List[KernelMetadata]: +def _ensure_kernel_files_available( + kernel_dir: Path, isotope: str +) -> List[KernelMetadata]: """Ensure that kernel CSVs for the requested isotope are available locally.""" kernels = _discover_kernel_files(kernel_dir=kernel_dir, isotope=isotope) if kernels: @@ -175,7 +179,11 @@ def _ensure_kernel_files_available(kernel_dir: Path, isotope: str) -> List[Kerne _CSV_PATTERN.match(path.name) is not None for path in kernel_dir.rglob("*.csv") ) if not has_any_csv_kernels: - logger.info("No kernel CSV files found under %s. Downloading %s kernels.", kernel_dir, isotope) + logger.info( + "No kernel CSV files found under %s. Downloading %s kernels.", + kernel_dir, + isotope, + ) else: logger.info( "No local kernel CSVs found for %s under %s. Downloading isotope archive.", @@ -196,7 +204,9 @@ def _ensure_kernel_files_available(kernel_dir: Path, isotope: str) -> List[Kerne def _load_octant_kernel(csv_path: Path) -> numpy.ndarray: """Load the positive octant stored in a kernel CSV.""" with csv_path.open("r", encoding="utf-8", newline="") as file_obj: - rows = [row for row in csv.reader(file_obj) if any(cell.strip() for cell in row)] + rows = [ + row for row in csv.reader(file_obj) if any(cell.strip() for cell in row) + ] if not rows: raise ValueError(f"Kernel CSV '{csv_path}' is empty.") @@ -214,7 +224,9 @@ def _load_octant_kernel(csv_path: Path) -> numpy.ndarray: ) num_sections = len(rows) // section_size - octant = numpy.empty((section_size, section_size, num_sections), dtype=numpy.float64) + octant = numpy.empty( + (section_size, section_size, num_sections), dtype=numpy.float64 + ) for section_idx in range(num_sections): start = section_idx * section_size diff --git a/tests/test_dvk.py b/tests/test_dvk.py new file mode 100644 index 0000000..b6d5bc0 --- /dev/null +++ b/tests/test_dvk.py @@ -0,0 +1,93 @@ +"""Tests for DoseVoxelKernel CSV loading and selection.""" + +from pathlib import Path + +import numpy as np +import pytest + +import pytheranostics.dosimetry.dvk as dvk +from pytheranostics.dosimetry.dvk import ( + DoseVoxelKernel, + KernelMetadata, + _build_full_kernel_from_octant, + _crop_kernel_around_center, + _select_closest_kernel, +) + + +def _write_kernel_csv(path: Path, octant: np.ndarray) -> None: + """Write a synthetic kernel octant using the Zenodo CSV layout.""" + path.parent.mkdir(parents=True, exist_ok=True) + + with path.open("w", encoding="utf-8", newline="") as file_obj: + for section_idx in range(octant.shape[2]): + block = octant[:, :, section_idx].T + for row in block: + file_obj.write(",".join(f"{value:.6f}" for value in row)) + file_obj.write("\n") + + if section_idx != octant.shape[2] - 1: + file_obj.write(",".join([""] * octant.shape[1])) + file_obj.write("\n") + + +def test_select_closest_kernel_warns_for_large_mismatch(caplog: pytest.LogCaptureFixture): + """A warning should be emitted when the requested voxel size is far from a match.""" + kernels = [ + KernelMetadata(Path("a.csv"), "Lu177", 4.7952, 173), + KernelMetadata(Path("b.csv"), "Lu177", 10.0, 83), + ] + + with caplog.at_level("WARNING"): + selected = _select_closest_kernel(4.5, kernels) + + assert selected.voxel_size_mm == pytest.approx(4.7952) + assert "does not closely match" in caplog.text + + +def test_dose_voxel_kernel_reconstructs_octant_and_crops_by_default( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +): + """DoseVoxelKernel should rebuild the full kernel and crop it near 25 mm.""" + kernel_dir = tmp_path / "voxel_kernels" + octant = np.arange(1, 65, dtype=np.float64).reshape(4, 4, 4) + kernel_path = kernel_dir / "177Lu" / "177LU_6.647D_99%_10mm_7x7x7.csv" + _write_kernel_csv(kernel_path, octant) + + monkeypatch.setattr(dvk, "_DATA_DIR", kernel_dir) + + kernel = DoseVoxelKernel(isotope="Lu177", voxel_size_mm=10.0) + expected_full = _build_full_kernel_from_octant(octant) + expected_cropped = _crop_kernel_around_center(expected_full, 10.0, 25.0) + + assert kernel.kernel.shape == (3, 3, 3) + assert np.array_equal(kernel.kernel, expected_cropped) + assert kernel.kernel[1, 1, 1] == pytest.approx(octant[0, 0, 0]) + assert np.array_equal(kernel.kernel, np.flip(kernel.kernel, axis=0)) + assert np.array_equal(kernel.kernel, np.flip(kernel.kernel, axis=1)) + assert np.array_equal(kernel.kernel, np.flip(kernel.kernel, axis=2)) + + +def test_dose_voxel_kernel_downloads_when_no_csv_exists( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +): + """The loader should invoke the download helper when no CSV kernels are present.""" + kernel_dir = tmp_path / "voxel_kernels" + octant = np.arange(1, 9, dtype=np.float64).reshape(2, 2, 2) + created = {"called": False} + + def _fake_download(isotope: str, kernel_dir_arg: Path) -> None: + created["called"] = True + assert isotope == "Lu177" + _write_kernel_csv( + kernel_dir_arg / "177Lu" / "177LU_6.647D_99%_5mm_3x3x3.csv", + octant, + ) + + monkeypatch.setattr(dvk, "_DATA_DIR", kernel_dir) + monkeypatch.setattr(dvk, "_download_kernels_from_zenodo", _fake_download) + + kernel = DoseVoxelKernel(isotope="Lu177", voxel_size_mm=5.0, crop_kernel_size_mm=None) + + assert created["called"] is True + assert kernel.kernel.shape == (3, 3, 3) diff --git a/tests/test_resource_loading.py b/tests/test_resource_loading.py index 57ec9f0..daa22a0 100644 --- a/tests/test_resource_loading.py +++ b/tests/test_resource_loading.py @@ -1,8 +1,5 @@ """Tests for data access via importlib.resources-based helpers.""" -import numpy as np - -from pytheranostics.dosimetry.dvk import DoseVoxelKernel from pytheranostics.dosimetry.olinda import load_phantom_mass @@ -10,10 +7,3 @@ def test_load_phantom_mass_returns_expected_value(): """The packaged phantom mass table should include standard organs.""" liver_mass = load_phantom_mass(gender="Male", organ="Liver") assert liver_mass == 1800 # matches bundled phantom data - - -def test_dose_voxel_kernel_falls_back_to_packaged_kernel(): - """DoseVoxelKernel should load the packaged default kernel if the exact voxel size is missing.""" - kernel = DoseVoxelKernel(isotope="Lu177", voxel_size_mm=5.5) - assert kernel.kernel.shape == (51, 51, 51) - assert kernel.kernel.dtype == np.float64 From 15bf390eb7707b76d39318331d04a31bc32f5745 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 24 Mar 2026 13:17:18 -0700 Subject: [PATCH 11/27] Update to resample TIA to kernel grid --- pytheranostics/dosimetry/dvk.py | 3 + pytheranostics/dosimetry/voxel_s_dosimetry.py | 187 ++++++++++++++++-- .../imaging_ds/longitudinal_study.py | 6 +- tests/test_dvk.py | 8 +- 4 files changed, 188 insertions(+), 16 deletions(-) diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index eb2eb53..20e1c9a 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -332,6 +332,9 @@ def __init__( voxel_size_mm=selected_kernel.voxel_size_mm, crop_size_mm=crop_kernel_size_mm, ).astype(numpy.float64) + self.voxel_size_mm = float(selected_kernel.voxel_size_mm) + self.matrix_size = int(selected_kernel.matrix_size) + self.isotope = selected_kernel.isotope def tia_to_dose( self, tia_mbq_s: numpy.ndarray, ct: Optional[numpy.ndarray] = None diff --git a/pytheranostics/dosimetry/voxel_s_dosimetry.py b/pytheranostics/dosimetry/voxel_s_dosimetry.py index 16d929d..7765c14 100644 --- a/pytheranostics/dosimetry/voxel_s_dosimetry.py +++ b/pytheranostics/dosimetry/voxel_s_dosimetry.py @@ -1,8 +1,10 @@ """Module for voxel-level dosimetry calculations.""" +import logging import os import shutil -from typing import Any, Dict, Optional +from pathlib import Path +from typing import Any, Dict, Optional, Tuple import numpy import pandas @@ -16,6 +18,8 @@ from pytheranostics.imaging_tools.tools import itk_image_from_array, resample_to_target from pytheranostics.shared.resources import resource_path +logger = logging.getLogger(__name__) + class VoxelSDosimetry(BaseDosimetry): """Voxel S Dosimetry class. @@ -125,20 +129,44 @@ def compute_voxel_tia(self) -> None: def apply_voxel_s(self) -> None: """Apply convolution over TIA map.""" ref_time_id = self.config["ReferenceTimePoint"] - nm_voxel_mm = self.nm_data.images[ref_time_id].GetSpacing()[0] + output_grid = str(self.config.get("VoxelSOutputGrid", "NM")).upper() + logger.info("Applying voxel-S dosimetry using %s output grid.", output_grid) + output_ref_image, output_mask = self._resolve_output_grid_reference( + ref_time_id=ref_time_id, output_grid=output_grid + ) + tia_ref_image = self.tia_map.images[0] + tia_voxel_mm = self._scalar_voxel_size_mm(tia_ref_image, image_name="TIA map") + logger.info("Selecting dose kernel from TIA map spacing %.3f mm.", tia_voxel_mm) + + if self.nm_data.meta[0].Radionuclide is None: + raise ValueError( + "Radionuclide information is required in nm_data meta to apply voxel S-value convolution." + ) dose_kernel = DoseVoxelKernel( - isotope=self.nm_data.meta[0].Radionuclide, voxel_size_mm=nm_voxel_mm + isotope=self.nm_data.meta[0].Radionuclide, voxel_size_mm=tia_voxel_mm + ) + logger.info( + "Using %s kernel with voxel size %.3f mm and matrix size %d.", + dose_kernel.isotope, + dose_kernel.voxel_size_mm, + dose_kernel.matrix_size, + ) + kernel_ref_image = self._build_reference_image_with_spacing( + ref_image=tia_ref_image, + spacing_mm=(dose_kernel.voxel_size_mm, dose_kernel.voxel_size_mm, dose_kernel.voxel_size_mm), ) + tia_kernel_grid = self._resample_tia_to_target_grid(target_img=kernel_ref_image) - # Resample CT to NM (Default using linear interpolator) + logger.info("Resampling CT to the kernel grid for density scaling.") resampled_ct = resample_to_target( source_img=self.ct_data.images[ref_time_id], - target_img=self.nm_data.images[ref_time_id], + target_img=kernel_ref_image, ) - dose_map_array = dose_kernel.tia_to_dose( - tia_mbq_s=self.tia_map.array_at(0) * self.toMBqs, + logger.info("Computing dose on the kernel grid.") + kernel_dose_array = dose_kernel.tia_to_dose( + tia_mbq_s=tia_kernel_grid * self.toMBqs, ct=( numpy.transpose( SimpleITK.GetArrayFromImage(resampled_ct), axes=(1, 2, 0) @@ -147,25 +175,150 @@ def apply_voxel_s(self) -> None: else None ), ) + kernel_dose_img = itk_image_from_array( + array=numpy.transpose(kernel_dose_array, axes=(2, 0, 1)), + ref_image=kernel_ref_image, + ) + logger.info("Resampling dose map from kernel grid to %s output grid.", output_grid) + dose_map_array = numpy.transpose( + SimpleITK.GetArrayFromImage( + resample_to_target( + source_img=kernel_dose_img, + target_img=output_ref_image, + default_value=0.0, + ) + ), + axes=(1, 2, 0), + ) # Create ITK Image Object and embed it into a LongitudinalStudy - # Clear dose outside patient body: - dose_map_array *= self.nm_data.masks[ref_time_id]["WholeBody"] + # Clear dose outside patient body on the chosen output grid when a mask exists. + if output_mask is not None: + logger.info("Applying WholeBody mask on the %s output grid.", output_grid) + dose_map_array *= output_mask + logger.info("Storing voxel-S dose map on the %s output grid.", output_grid) self.dose_map = LongitudinalStudy( images={ 0: itk_image_from_array( array=numpy.transpose(dose_map_array, axes=(2, 0, 1)), - ref_image=self.nm_data.images[ref_time_id], + ref_image=output_ref_image, ) }, meta={0: self.nm_data.meta[ref_time_id]}, ) - self.dose_map.masks[0] = self.nm_data.masks[0].copy() + if output_grid == "NM" and ref_time_id in self.nm_data.masks: + self.dose_map.masks[0] = self.nm_data.masks[ref_time_id].copy() + elif output_grid == "CT" and ref_time_id in self.ct_data.masks: + self.dose_map.masks[0] = self.ct_data.masks[ref_time_id].copy() return None + @staticmethod + def _build_reference_image_with_spacing( + ref_image: SimpleITK.Image, spacing_mm: Tuple[float, float, float] + ) -> SimpleITK.Image: + """Create a blank reference image using a new spacing on the same physical frame.""" + original_spacing = ref_image.GetSpacing()[:3] + original_size = ref_image.GetSize()[:3] + new_size = [ + max( + 1, + int( + round( + original_size[idx] * original_spacing[idx] / spacing_mm[idx] + ) + ), + ) + for idx in range(3) + ] + target_img = SimpleITK.Image(new_size, ref_image.GetPixelID()) + target_img.SetSpacing(spacing_mm) + target_img.SetOrigin(ref_image.GetOrigin()) + target_img.SetDirection(ref_image.GetDirection()) + return target_img + + @staticmethod + def _scalar_voxel_size_mm(image: SimpleITK.Image, image_name: str) -> float: + """Return a scalar voxel size for isotropic-kernel selection.""" + spacing = tuple(float(value) for value in image.GetSpacing()[:3]) + mean_spacing = float(numpy.mean(spacing)) + if not numpy.allclose(spacing, mean_spacing, atol=0.1): + logger.warning( + "%s spacing %s mm is anisotropic. Kernel selection will use the " + "mean voxel size %.3f mm and resample to the chosen isotropic grid.", + image_name, + spacing, + mean_spacing, + ) + return mean_spacing + + def _resample_tia_to_target_grid( + self, target_img: SimpleITK.Image + ) -> numpy.ndarray: + """Resample TIA totals per voxel through a temporary density representation.""" + logger.info( + "Resampling TIA map to kernel grid using voxel-volume normalization to preserve total activity." + ) + tia_array = self.tia_map.array_at(0).astype(numpy.float64) + source_voxel_volume_ml = self._voxel_volume_ml(self.tia_map.images[0]) + target_voxel_volume_ml = self._voxel_volume_ml(target_img) + + tia_density = tia_array / source_voxel_volume_ml + tia_density_img = itk_image_from_array( + array=numpy.transpose(tia_density, axes=(2, 0, 1)), + ref_image=self.tia_map.images[0], + ) + resampled_density_img = resample_to_target( + source_img=tia_density_img, + target_img=target_img, + default_value=0.0, + ) + resampled_density = numpy.transpose( + SimpleITK.GetArrayFromImage(resampled_density_img), + axes=(1, 2, 0), + ).astype(numpy.float64) + resampled_tia = resampled_density * target_voxel_volume_ml + + source_total = float(numpy.sum(tia_array)) + resampled_total = float(numpy.sum(resampled_tia)) + logger.info( + "TIA totals before renormalization: source=%.6g MBq h, resampled=%.6g MBq h.", + source_total, + resampled_total, + ) + if source_total > 0.0 and resampled_total > 0.0: + resampled_tia *= source_total / resampled_total + logger.info("Renormalized resampled TIA map to preserve total activity.") + + return resampled_tia + + @staticmethod + def _voxel_volume_ml(image: SimpleITK.Image) -> float: + """Return the voxel volume in mL from image spacing in mm.""" + spacing = image.GetSpacing()[:3] + return float(spacing[0] * spacing[1] * spacing[2] / 1000.0) + + def _resolve_output_grid_reference( + self, ref_time_id: int, output_grid: str + ) -> Tuple[SimpleITK.Image, Optional[numpy.ndarray]]: + """Return the reference image and optional body mask for the requested output grid.""" + if output_grid == "NM": + return ( + self.nm_data.images[ref_time_id], + self.nm_data.masks[ref_time_id].get("WholeBody"), + ) + if output_grid == "CT": + ct_mask = None + if ref_time_id in self.ct_data.masks: + ct_mask = self.ct_data.masks[ref_time_id].get("WholeBody") + return self.ct_data.images[ref_time_id], ct_mask + + raise ValueError( + f"VoxelSOutputGrid '{output_grid}' is not supported. Use 'NM' or 'CT'." + ) + def run_MC(self) -> None: # TODO: finish the code!!!!! """Run MC.""" raise NotImplementedError("MC is not implemmented yet.") @@ -243,6 +396,18 @@ def compute_dose(self) -> None: """ self.compute_tia() self.compute_voxel_tia() + + ref_time_id = self.config["ReferenceTimePoint"] + self.nm_data.save_image_to_nii_at( + time_id=ref_time_id, out_path=self.db_dir, name="_nm_ref" + ) + self.ct_data.save_image_to_nii_at( + time_id=ref_time_id, out_path=self.db_dir, name="_ct_ref" + ) + self.tia_map.save_image_to_nii_at( + time_id=0, out_path=self.db_dir, name="_tia_map" + ) + if self.config["Method"] == "Voxel-S-value": self.apply_voxel_s() elif self.config["Method"] == "Monte-Carlo": diff --git a/pytheranostics/imaging_ds/longitudinal_study.py b/pytheranostics/imaging_ds/longitudinal_study.py index e5cfbdb..e8ff1e0 100644 --- a/pytheranostics/imaging_ds/longitudinal_study.py +++ b/pytheranostics/imaging_ds/longitudinal_study.py @@ -800,11 +800,11 @@ def activity_in(self, region: str, time_id: int) -> float: raise AssertionError( "Can't compute activity if the image data does not represent the distribution of a radionuclide" ) - return numpy.sum( + return float(numpy.sum( self.masks[time_id][region] * self.array_at(time_id=time_id) * self.voxel_volume(time_id=time_id) - ) + )) def density_of(self, region: str, time_id: int) -> float: """Return the mean density of region of interest, in HU. @@ -971,7 +971,7 @@ def save_image_to_nii_at( """ logger.info(f"Writing Image ({name}) into nifty file.") SimpleITK.WriteImage( - image=SimpleITK.Cast(self.images[time_id], SimpleITK.sitkInt32), + image=self.images[time_id], fileName=out_path / f"Image_{time_id}{name}.nii.gz", ) return None diff --git a/tests/test_dvk.py b/tests/test_dvk.py index b6d5bc0..cd90dd2 100644 --- a/tests/test_dvk.py +++ b/tests/test_dvk.py @@ -31,7 +31,9 @@ def _write_kernel_csv(path: Path, octant: np.ndarray) -> None: file_obj.write("\n") -def test_select_closest_kernel_warns_for_large_mismatch(caplog: pytest.LogCaptureFixture): +def test_select_closest_kernel_warns_for_large_mismatch( + caplog: pytest.LogCaptureFixture, +): """A warning should be emitted when the requested voxel size is far from a match.""" kernels = [ KernelMetadata(Path("a.csv"), "Lu177", 4.7952, 173), @@ -87,7 +89,9 @@ def _fake_download(isotope: str, kernel_dir_arg: Path) -> None: monkeypatch.setattr(dvk, "_DATA_DIR", kernel_dir) monkeypatch.setattr(dvk, "_download_kernels_from_zenodo", _fake_download) - kernel = DoseVoxelKernel(isotope="Lu177", voxel_size_mm=5.0, crop_kernel_size_mm=None) + kernel = DoseVoxelKernel( + isotope="Lu177", voxel_size_mm=5.0, crop_kernel_size_mm=None + ) assert created["called"] is True assert kernel.kernel.shape == (3, 3, 3) From 5bdf9b78287b0927a312d7b62778c4752c22a7b7 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 24 Mar 2026 14:57:13 -0700 Subject: [PATCH 12/27] Patch NM data loading to correct count/activity if reference is different than acquisition START --- pytheranostics/imaging_tools/tools.py | 221 ++++++++++++++++++++------ 1 file changed, 175 insertions(+), 46 deletions(-) diff --git a/pytheranostics/imaging_tools/tools.py b/pytheranostics/imaging_tools/tools.py index 5f43e14..bccca61 100644 --- a/pytheranostics/imaging_tools/tools.py +++ b/pytheranostics/imaging_tools/tools.py @@ -4,6 +4,8 @@ import glob import logging +import math +from datetime import datetime from pathlib import Path from typing import TYPE_CHECKING, Dict, List, Optional, Tuple @@ -184,64 +186,191 @@ def itk_image_from_array( return image +def _parse_dicom_date_time( + date_val: Optional[str], time_val: Optional[str] +) -> Optional[datetime]: + """Parse DICOM DA/TM values into a Python datetime.""" + if not date_val: + return None + + date_str = str(date_val) + time_str = str(time_val or "000000").split(".")[0] + if len(time_str) < 6: + time_str = time_str.ljust(6, "0") + + try: + return datetime.strptime(f"{date_str}{time_str[:6]}", "%Y%m%d%H%M%S") + except ValueError: + return None + + +def _parse_dicom_datetime(dt_val: Optional[str]) -> Optional[datetime]: + """Parse a DICOM DT value into a Python datetime.""" + if not dt_val: + return None + + value = str(dt_val).split(".")[0] + if len(value) < 8: + return None + + return _parse_dicom_date_time(value[:8], value[8:14] if len(value) > 8 else None) + + +def _get_acquisition_start_datetime(ds: pydicom.Dataset) -> Optional[datetime]: + """Return the best available acquisition-start datetime from a DICOM dataset.""" + for date_key, time_key in [ + ("AcquisitionDate", "AcquisitionTime"), + ("SeriesDate", "SeriesTime"), + ("ContentDate", "ContentTime"), + ("StudyDate", "StudyTime"), + ]: + dt = _parse_dicom_date_time( + getattr(ds, date_key, None), getattr(ds, time_key, None) + ) + if dt is not None: + return dt + return None + + +def _get_radiopharm_admin_datetime_and_half_life( + ds: pydicom.Dataset, +) -> Tuple[Optional[datetime], Optional[float]]: + """Return radiopharmaceutical administration datetime and half-life in seconds.""" + if not hasattr(ds, "RadiopharmaceuticalInformationSequence"): + return None, None + + rp_seq = ds.RadiopharmaceuticalInformationSequence + if len(rp_seq) == 0: + return None, None + + rp_info = rp_seq[0] + admin_dt = _parse_dicom_datetime( + getattr(rp_info, "RadiopharmaceuticalStartDateTime", None) + ) + if admin_dt is None: + admin_date = getattr(rp_info, "RadiopharmaceuticalStartDate", None) + if admin_date is None: + admin_date = getattr(ds, "StudyDate", None) + admin_dt = _parse_dicom_date_time( + admin_date, + getattr(rp_info, "RadiopharmaceuticalStartTime", None), + ) + + half_life_seconds = None + half_life_val = getattr(rp_info, "RadionuclideHalfLife", None) + if half_life_val is not None: + try: + half_life_seconds = float(half_life_val) + except (TypeError, ValueError): + half_life_seconds = None + + return admin_dt, half_life_seconds + + def apply_qspect_dcm_scaling( image: Image, dir: str, scale_factor: Optional[Tuple[float, float]] = None ) -> Image: - """Read dicom metadata to extract appropriate scaling for Image voxel values. + """Read DICOM metadata to scale quantitative NM/PT images and harmonize decay reference. - Apply scaling to original image and generate a new SimpleITK image object. + NM images are converted using the Real World Value Mapping when available. NM and PT + images are then optionally converted from ADMIN-referenced decay correction to START. + """ + path_dir = Path(dir) + dicom_files = sorted(path_dir.glob("*.dcm")) + if len(dicom_files) == 0: + raise AssertionError(f"No Dicom data was found under {dir}") - Args: - image (Image): Original SimpleITK image. - dir (str): Directory path containing DICOM files. - scale_factor (Optional[Tuple[float, float]], optional): Custom scale factor. Defaults to None. + dcm_data = pydicom.dcmread(str(dicom_files[0]), stop_before_pixels=True, force=True) + modality = getattr(dcm_data, "Modality", None) - Raises - ------ - AssertionError - If wrong modality or multiple DICOM files found. + if modality not in ["NM", "PT"]: + raise AssertionError( + f"Wrong Modality, expecting NM/PT quantitative data, but got {modality}" + ) - Returns - ------- - Image - Scaled SimpleITK image. - """ - if scale_factor is None: - # We use pydicom to access the appropriate tag: - # First, find the SPECT dicom file: - path_dir = Path(dir) - nm_files = [files for files in path_dir.glob("*.dcm")] - dcm_data = pydicom.dcmread(str(nm_files[0])) - - if ( - dcm_data.Modality == "PT" - ): # PET modality stores individual dicom files for each slice, similar to CT. - # Its scaling is readed correctly from SimpleITK, so nothing to do here. - return image - - if dcm_data.Modality != "NM": - raise AssertionError( - f"Wrong Modality, expecting NM for SPECT data, but got {dcm_data.Modality}" - ) + if modality == "NM" and scale_factor is None and len(dicom_files) != 1: + raise AssertionError( + f"Found more than 1 .dcm file inside {path_dir.name}, not sure which one is the right SPECT." + ) - if len(nm_files) != 1: - raise AssertionError( - f"Found more than 1 .dcm file inside {path_dir.name}, not sure which one is the right SPECT." - ) + image_array = numpy.squeeze(SimpleITK.GetArrayFromImage(image)).astype(numpy.float32) - # If scale_factor is not provided by the user, we assume it is a Q-SPECT file. - slope = dcm_data.RealWorldValueMappingSequence[0].RealWorldValueSlope - intercept = dcm_data.RealWorldValueMappingSequence[0].RealWorldValueIntercept + if scale_factor is not None: + slope = float(scale_factor[0]) + intercept = float(scale_factor[1]) + logger.info( + "Applying user-provided quantitative scaling for %s: slope=%s intercept=%s.", + modality, + slope, + intercept, + ) + image_array = slope * image_array + intercept + elif modality == "NM": + slope = float(dcm_data.RealWorldValueMappingSequence[0].RealWorldValueSlope) + intercept = float( + dcm_data.RealWorldValueMappingSequence[0].RealWorldValueIntercept + ) + logger.info( + "Applying DICOM quantitative scaling for NM: slope=%s intercept=%s.", + slope, + intercept, + ) + image_array = slope * image_array + intercept else: - slope = scale_factor[0] - intercept = scale_factor[1] + logger.info( + "PT quantitative values are assumed to be scaled correctly by SimpleITK; no slope/intercept scaling applied." + ) - # Re-scale voxel values - image_array = numpy.squeeze(SimpleITK.GetArrayFromImage(image)) - image_array = slope * image_array.astype(numpy.float32) + intercept + decay_correction = str(getattr(dcm_data, "DecayCorrection", "") or "").upper() + logger.info("DICOM DecayCorrection for %s image is '%s'.", modality, decay_correction or "MISSING") + + if decay_correction == "START": + logger.info("No additional decay normalization is needed because the image is already referenced to acquisition start.") + elif decay_correction == "ADMIN": + acquisition_dt = _get_acquisition_start_datetime(dcm_data) + admin_dt, half_life_seconds = _get_radiopharm_admin_datetime_and_half_life(dcm_data) + + if acquisition_dt is None: + logger.warning( + "DecayCorrection is ADMIN but acquisition start time could not be resolved. Skipping decay normalization to START." + ) + elif admin_dt is None: + logger.warning( + "DecayCorrection is ADMIN but radiopharmaceutical administration time could not be resolved. Skipping decay normalization to START." + ) + elif half_life_seconds is None or half_life_seconds <= 0: + logger.warning( + "DecayCorrection is ADMIN but RadionuclideHalfLife is missing or invalid (%s). Skipping decay normalization to START.", + half_life_seconds, + ) + else: + delta_seconds = (acquisition_dt - admin_dt).total_seconds() + if delta_seconds < 0: + logger.warning( + "DecayCorrection is ADMIN but acquisition start (%s) is earlier than administration time (%s). Skipping decay normalization to START.", + acquisition_dt.isoformat(), + admin_dt.isoformat(), + ) + else: + decay_factor = math.exp(-math.log(2.0) * delta_seconds / half_life_seconds) + logger.info( + "Converting %s image from ADMIN to START decay reference using administration time %s, acquisition time %s, half-life %.6g s, elapsed time %.3f s, factor %.9g.", + modality, + admin_dt.isoformat(), + acquisition_dt.isoformat(), + half_life_seconds, + delta_seconds, + decay_factor, + ) + image_array *= decay_factor + elif decay_correction: + logger.info( + "DecayCorrection '%s' is not explicitly handled. Image values are left unchanged.", + decay_correction, + ) + else: + logger.info("DecayCorrection tag is missing. Image values are left unchanged.") - # Generate re-scaled SimpleITK.Image object by building a new Image from re_scaled array, and copying - # metadata from original image. return itk_image_from_array(array=image_array, ref_image=image) From fc782d93af12c3475e95394d417fccef5a55788e Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 24 Mar 2026 15:12:56 -0700 Subject: [PATCH 13/27] Better documentation of mass-density reweighting of kernel --- pytheranostics/dosimetry/dvk.py | 62 +++++++++++++++++++++++++-------- 1 file changed, 48 insertions(+), 14 deletions(-) diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index 20e1c9a..1a193c2 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -357,9 +357,9 @@ def tia_to_dose( if ct is not None: logger.warning( - "Scaling dose by density will yield erroneous dose values in very " - "low-density voxels (for example air inside the body). Please use " - "at your own risk." + "Applying local mass-density reweighting to a homogeneous-medium dose " + "kernel result. This is not a full heterogeneity correction and is " + "only a rough approximation in soft tissue." ) dose_mGy = self.weight_dose_by_density(dose_map=dose_mGy, ct=ct) @@ -368,20 +368,54 @@ def tia_to_dose( def weight_dose_by_density( self, dose_map: numpy.ndarray, ct: numpy.ndarray ) -> numpy.ndarray: - """Scale dose per voxel by voxel density. + """Apply local mass-density reweighting to a dose map. + + This method does not perform a true heterogeneity correction. It assumes the + dose kernel was computed in a homogeneous medium close to soft tissue and + rescales the resulting dose voxel-by-voxel by ``1 / rho`` using CT-derived + density. As a result, it should be treated only as a rough local mass + correction in near-water soft tissues. + + Methodological limitations: + - It uses only the local density of the target voxel and ignores transport + effects from surrounding tissues. + - It does not account for material composition differences beyond the + HU-to-density mapping. + - Negative HU values are clipped to 0 HU before conversion so air and other + low-density voxels are effectively treated as water-equivalent for this + scaling step. This avoids unphysical dose blow-up but is not physically + rigorous. - This is only valid for voxels of density similar to that of soft tissue and will also improve results for voxels - with higher density of soft tissue in some instances. However, it will over-estimate doses in voxels with lower density than soft tissue. - To prevent dose to shoot-up in areas of air where there is activity present (e.g., in the patient's gut), we do not apply scaling based on density in those voxels (i.e., we apply a factor of 1, which is equivalent to saying - the tissue is ~ soft tissue). - - Args: - dose_map (numpy.ndarray): Dose-map obtained from convolution of TIA map and Dose Kernel. - ct (numpy.ndarray): CT image, in HU. + Parameters + ---------- + dose_map : numpy.ndarray + Dose map obtained from convolution of the TIA map with a homogeneous- + medium dose kernel. + ct : numpy.ndarray + CT image in HU, sampled on the same grid as ``dose_map``. Returns ------- numpy.ndarray - Modified Dose-map with dose per voxel scaled-up by density. + Dose map after local density reweighting. + + Raises + ------ + ValueError + If ``dose_map`` and ``ct`` do not share the same shape. """ - return 1 / hu_to_rho(hu=numpy.clip(ct, 0, 99999)) * dose_map + if dose_map.shape != ct.shape: + raise ValueError( + "Dose-map and CT array must have the same shape for density weighting. " + f"Got {dose_map.shape} and {ct.shape}." + ) + + ct_clipped = numpy.clip(ct.astype(numpy.float64), 0.0, None) + rho = hu_to_rho(hu=ct_clipped) + + logger.info( + "Applying local density reweighting with HU clipped to [0, inf) before " + "HU-to-density conversion." + ) + + return dose_map / rho From 1e73a1df0b098d26f6c32c82c7630a98992f275b Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 24 Mar 2026 16:02:45 -0700 Subject: [PATCH 14/27] End-to-end validation test --- .../data/voxel_dosimetry_validation/README.md | 17 ++ .../dosimetry_fit_defaults.json | 11 + .../expected_df_ad.csv | 7 + .../expected_results.csv | 7 + .../rtstructs/rtstruct_scan1.dcm | Bin 0 -> 1100338 bytes .../rtstructs/rtstruct_scan2.dcm | Bin 0 -> 1096722 bytes .../rtstructs/rtstruct_scan3.dcm | Bin 0 -> 1080762 bytes .../rtstructs/rtstruct_scan4.dcm | Bin 0 -> 1102238 bytes .../voi_mappings_config.json | 64 +++++ tests/test_voxel_dosimetry_validation.py | 268 ++++++++++++++++++ 10 files changed, 374 insertions(+) create mode 100644 tests/data/voxel_dosimetry_validation/README.md create mode 100644 tests/data/voxel_dosimetry_validation/dosimetry_fit_defaults.json create mode 100644 tests/data/voxel_dosimetry_validation/expected_df_ad.csv create mode 100644 tests/data/voxel_dosimetry_validation/expected_results.csv create mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan1.dcm create mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan2.dcm create mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan3.dcm create mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan4.dcm create mode 100644 tests/data/voxel_dosimetry_validation/voi_mappings_config.json create mode 100644 tests/test_voxel_dosimetry_validation.py diff --git a/tests/data/voxel_dosimetry_validation/README.md b/tests/data/voxel_dosimetry_validation/README.md new file mode 100644 index 0000000..3606e7c --- /dev/null +++ b/tests/data/voxel_dosimetry_validation/README.md @@ -0,0 +1,17 @@ +# Voxel Dosimetry Validation Assets + +This directory stores optional assets for the slow voxel dosimetry validation test: + +- `rtstructs/`: precomputed RT-STRUCT DICOM files for the SNMMI dosimetry dataset +- `expected_results.csv`: reference subset of `VoxelSDosimetry.results` +- `expected_df_ad.csv`: reference subset of `VoxelSDosimetry.df_ad` +- `voi_mappings_config.json` (optional): custom ROI mapping overrides for the validation run +- `dosimetry_fit_defaults.json` (optional): custom ROI fit defaults for the validation run + +The validation test is implemented in `tests/test_voxel_dosimetry_validation.py`. +It will skip automatically unless the RT-STRUCT directory and both CSV files are present. + +Recommended contents of the CSV files: +- Include only stable, comparable columns. +- Avoid object/list-valued columns such as raw fit-parameter arrays unless you serialize and compare them intentionally. +- Keep the index as ROI names so it matches the DataFrame produced by the pipeline. diff --git a/tests/data/voxel_dosimetry_validation/dosimetry_fit_defaults.json b/tests/data/voxel_dosimetry_validation/dosimetry_fit_defaults.json new file mode 100644 index 0000000..802680a --- /dev/null +++ b/tests/data/voxel_dosimetry_validation/dosimetry_fit_defaults.json @@ -0,0 +1,11 @@ +{ + "_description": "Apply bi-exponential fitting with uptake phase to all organs by default. Users can override these defaults for specific organs or ROIs by providing a custom configuration file.", + "organ_defaults": { + "fit_order": 2, + "with_uptake": true, + "param_init": {"A1": 100, "A2": 0.01}, + "fixed_parameters": null, + "bounds": {"A2": [0.004345, 1.0]}, + "washout_ratio": null + } +} diff --git a/tests/data/voxel_dosimetry_validation/expected_df_ad.csv b/tests/data/voxel_dosimetry_validation/expected_df_ad.csv new file mode 100644 index 0000000..9c4fb54 --- /dev/null +++ b/tests/data/voxel_dosimetry_validation/expected_df_ad.csv @@ -0,0 +1,7 @@ +,AD[Gy],AD[Gy/GBq] +Kidney_Left,2.741417121338495,0.3802018457101281 +Kidney_Right,2.2888928278245335,0.31744212546052364 +Liver,2.347015734546143,0.3255030791335655 +Spleen,3.017681578796798,0.41851642973026076 +RemainderOfBody,0.037553347066800524,0.005208201173128821 +WholeBody,0.10660231643052528,0.014784469371108286 diff --git a/tests/data/voxel_dosimetry_validation/expected_results.csv b/tests/data/voxel_dosimetry_validation/expected_results.csv new file mode 100644 index 0000000..7a6ca88 --- /dev/null +++ b/tests/data/voxel_dosimetry_validation/expected_results.csv @@ -0,0 +1,7 @@ +,Time_hr,Volume_CT_mL,Activity_MBq,Density_HU,Fit_params,R_squared_AIC,TIA_MBq_h,TIA_h,Lambda_eff +Kidney_Left,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(233.60824584960938), np.float64(234.09175872802734), np.float64(247.64728546142578), np.float64(248.5828399658203)]","[98.561432, 73.599408, 25.39568, 19.046609999999998]","[20.60774996325871, 21.191136749734177, 22.187848750563198, 20.703438989020096]","[108.74206751059795, 0.01408142600216577, -108.74206751059795, 0.8454291282326525]","[np.float64(0.999997766586273), np.float64(-18.02683458426659)]",7593.752576123306,1.0531628780002906,"[0.01408142600216577, 0.8454291282326525]" +Kidney_Right,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(250.99468231201172), np.float64(226.09806060791016), np.float64(240.1714324951172), np.float64(231.58836364746094)]","[92.891448, 66.860492, 23.209494, 15.666981999999999]","[21.532457910155138, 21.68748655522796, 22.7863626617111, 20.896461838756704]","[100.10353294428627, 0.014525034301202456, -100.10353294428627, 1.0572026378865176]","[np.float64(0.9996469559443484), np.float64(1.8552701404453744)]",6797.106045193271,0.9426775092905898,"[0.014525034301202456, 1.0572026378865176]" +Liver,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(2037.3516082763672), np.float64(2166.8357849121094), np.float64(2008.5783004760742), np.float64(2248.795509338379)]","[378.4744, 347.40441599999997, 192.66407999999998, 173.225408]","[51.76884621109779, 52.70464004098426, 54.272499497898295, 52.29396916837042]","[426.6982953753453, 0.0074761967214427676, -426.6982953753453, 0.6575597175063645]","[np.float64(0.9987334394598945), np.float64(15.394510161868892)]",56425.334754817464,7.825520695704643,"[0.0074761967214427676, 0.6575597175063645]" +Spleen,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(268.9018249511719), np.float64(273.8170623779297), np.float64(233.95729064941406), np.float64(235.11028289794922)]","[87.20683199999999, 77.633392, 34.655491999999995, 27.94343]","[41.40656253989871, 40.55778112135081, 39.93342627240932, 39.334509655986466]","[104.30127630893381, 0.01065558561011114, -104.30127630893381, 0.5562306512948885]","[np.float64(0.999990386007488), np.float64(-14.177557942224823)]",9600.898902876934,1.3315301254002192,"[0.01065558561011114, 0.5562306512948885]" +RemainderOfBody,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(94709.14363861084), np.float64(94599.15733337402), np.float64(94769.64569091797), np.float64(94535.92300415039)]","[671.643776, 317.521728, 150.297136, 121.737264]","[-689.8872685442167, -700.6898718764443, -695.1787159142423, -688.035938043804]","[668.6311458174671, 0.017797843153233038, -668.6311458174671, 36.97507466187187]","[np.float64(0.9240237820407323), np.float64(38.79977783207139)]",37550.016394303595,5.207739248593175,"[0.017797843153233038, 36.97507466187187]" +WholeBody,"[3.741111111111111, 27.725277777777777, 103.09416666666667, 123.98361111111112]","[np.float64(97500.0), np.float64(97500.0), np.float64(97500.0), np.float64(97500.0)]","[1328.7776, 883.018816, 426.22236799999996, 357.619616]","[-668.8390573941745, -678.4564056396484, -674.3848197350135, -665.7156941047082]","[1332.422362995897, 0.011439505602835902, -1332.422362995897, 32.95741948285121]","[np.float64(0.9806421746620866), np.float64(37.97065402607848)]",116435.09134326481,16.148157932464656,"[0.011439505602835902, 32.95741948285121]" diff --git 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ze*T+(?*~8p;kWO<`Afg`tH1Vx-~GLB+vokwU;gJm__crWqwoLD?|l2=o4@#jAASG) z-;dw??eG8UuiyRVkAC=j-~NqX{o(I?{~P!J$?T4+{%8OH+KW-i7(E^T_I35*>FCAy px38-g None: + missing = [ + path.name + for path in (_RTSTRUCTS_DIR, _EXPECTED_RESULTS_CSV, _EXPECTED_DF_AD_CSV) + if not path.exists() + ] + if missing: + pytest.skip( + 'Voxel dosimetry validation assets are not available. Missing: ' + + ', '.join(missing) + + f'. Populate {_VALIDATION_ASSETS} to enable this test.' + ) + + +def _copy_optional_validation_configs(project_base: Path) -> None: + for config_name in _OPTIONAL_CONFIG_FILES: + src = _VALIDATION_ASSETS / config_name + if src.exists(): + shutil.copy2(src, project_base / config_name) + + +def _copy_rtstruct_assets(project_base: Path) -> None: + target_dir = project_base / 'rtstructs' + shutil.copytree(_RTSTRUCTS_DIR, target_dir, dirs_exist_ok=True) + + +def _load_reference_frame(csv_path: Path) -> pd.DataFrame: + return pd.read_csv(csv_path, index_col=0) + + +def _parse_numeric_list_cell(value: object) -> object: + if not isinstance(value, str): + return value + + stripped = value.strip() + if not (stripped.startswith('[') and stripped.endswith(']')): + return value + + inner = stripped[1:-1].strip() + if not inner: + return [] + + cleaned = re.sub(r"np\.float64\(([^()]*)\)", r"\1", inner) + return [float(item.strip()) for item in cleaned.split(',')] + + +def _normalize_sequence_cell(value: object) -> object: + if isinstance(value, np.ndarray): + return [float(item) for item in value.tolist()] + if isinstance(value, (list, tuple)): + return [float(item) for item in value] + return value + + +def _normalize_frame_cells(frame: pd.DataFrame) -> pd.DataFrame: + normalized = frame.copy() + for column in normalized.columns: + if column in _LIST_LIKE_RESULTS_COLUMNS: + normalized[column] = normalized[column].map(_parse_numeric_list_cell) + normalized[column] = normalized[column].map(_normalize_sequence_cell) + return normalized + + +def _assert_sequence_columns_close( + actual: pd.DataFrame, expected: pd.DataFrame, *, rtol: float, atol: float +) -> None: + for column in expected.columns: + if column not in _LIST_LIKE_RESULTS_COLUMNS: + continue + for roi_name in expected.index: + expected_value = expected.at[roi_name, column] + actual_value = actual.at[roi_name, column] + + if isinstance(expected_value, list): + if not isinstance(actual_value, list): + raise AssertionError( + f"Column '{column}' for ROI '{roi_name}' is not list-like in actual results." + ) + if len(actual_value) != len(expected_value): + raise AssertionError( + f"Column '{column}' for ROI '{roi_name}' has different lengths: " + f"{len(actual_value)} != {len(expected_value)}" + ) + if not np.allclose(actual_value, expected_value, rtol=rtol, atol=atol): + raise AssertionError( + f"Column '{column}' for ROI '{roi_name}' differs. " + f"Actual={actual_value}, Expected={expected_value}" + ) + + +def _prepare_frame_for_comparison( + actual: pd.DataFrame, expected: pd.DataFrame +) -> tuple[pd.DataFrame, pd.DataFrame]: + missing_columns = [col for col in expected.columns if col not in actual.columns] + if missing_columns: + raise AssertionError( + 'Actual dataframe is missing expected columns: ' + + ', '.join(missing_columns) + ) + + actual_prepared = actual.loc[:, list(expected.columns)].copy() + expected_prepared = expected.copy() + + actual_prepared.index = actual_prepared.index.map(str) + expected_prepared.index = expected_prepared.index.map(str) + + missing_index = [idx for idx in expected_prepared.index if idx not in actual_prepared.index] + if missing_index: + raise AssertionError( + 'Actual dataframe is missing expected index entries: ' + ', '.join(missing_index) + ) + + actual_prepared = actual_prepared.loc[list(expected_prepared.index)] + actual_prepared = _normalize_frame_cells(actual_prepared) + expected_prepared = _normalize_frame_cells(expected_prepared) + + for column in expected_prepared.columns: + if column in _LIST_LIKE_RESULTS_COLUMNS: + continue + if pd.api.types.is_numeric_dtype(expected_prepared[column]): + actual_prepared[column] = pd.to_numeric(actual_prepared[column]) + + actual_prepared = actual_prepared.sort_index() + expected_prepared = expected_prepared.sort_index() + return actual_prepared, expected_prepared + + +@pytest.mark.integration +@pytest.mark.slow +def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: + _skip_if_validation_assets_missing() + + project_base = tmp_path / 'snmmi_dosimetry_validation_project' + init_project(project_base) + _copy_optional_validation_configs(project_base) + + try: + fetch_snmmi_dosimetry_challenge(data_home=str(project_base)) + except RuntimeError as exc: + pytest.skip(f'SNMMI dosimetry dataset could not be fetched: {exc}') + + _copy_rtstruct_assets(project_base) + + study_info, ct_paths, spect_paths, rtstruct_files = auto_setup_dosimetry_study_inventory( + base_dir=project_base, + patient_id=None, + ) + + assert study_info.get('patient_id') is not None + assert ct_paths, 'No CT timepoints were discovered by the validation pipeline.' + assert spect_paths, 'No SPECT timepoints were discovered by the validation pipeline.' + assert rtstruct_files, 'No RTSTRUCT files were discovered by the validation pipeline.' + + long_ct, long_spect, _, _ = create_studies_with_masks( + patient_id=study_info['patient_id'], + cycle_no=1, + parallel=True, + mapping_config=project_base / 'voi_mappings_config.json', + study_info=study_info, + ) + + roi_config = build_roi_fit_config( + longSPECT=long_spect, + config_path=project_base / 'dosimetry_fit_defaults.json', + ) + + first_tp_injection = study_info['time_points'][0]['injection_info'] + database_dir = project_base / 'dosimetry_database' + results_dir = project_base / 'results' + database_dir.mkdir(parents=True, exist_ok=True) + results_dir.mkdir(parents=True, exist_ok=True) + + dosimetry_config = { + 'PatientID': study_info['patient_id'], + 'Cycle': 1, + 'DatabaseDir': str(database_dir), + 'results_path': str(results_dir), + 'VOIs': roi_config, + 'InjectionDate': first_tp_injection['injection_date'], + 'InjectionTime': first_tp_injection['injection_time'], + 'InjectedActivity': long_spect.meta[0].Injected_Activity_MBq, + 'Radionuclide': long_spect.meta[0].Radionuclide, + 'PatientWeight_g': first_tp_injection['patient_weight_g'], + 'Level': 'Voxel', + 'Method': 'Voxel-S-value', + 'ScaleDoseByDensity': False, + 'ReferenceTimePoint': 0, + } + + dosimetry = VoxelSDosimetry( + config=dosimetry_config, + nm_data=long_spect, + ct_data=long_ct, + ) + dosimetry.compute_dose() + + expected_results = _load_reference_frame(_EXPECTED_RESULTS_CSV) + expected_df_ad = _load_reference_frame(_EXPECTED_DF_AD_CSV) + + actual_results, expected_results = _prepare_frame_for_comparison( + dosimetry.results, expected_results + ) + actual_df_ad, expected_df_ad = _prepare_frame_for_comparison( + dosimetry.df_ad, expected_df_ad + ) + + _assert_sequence_columns_close( + actual_results, + expected_results, + rtol=1e-4, + atol=1e-6, + ) + assert_frame_equal( + actual_results.drop(columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(actual_results.columns))), + expected_results.drop(columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(expected_results.columns))), + check_dtype=False, + rtol=1e-4, + atol=1e-6, + ) + assert_frame_equal( + actual_df_ad, + expected_df_ad, + check_dtype=False, + rtol=1e-4, + atol=1e-6, + ) From 971285006e56f9727c2380be400f1672884f3890 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:11:54 -0700 Subject: [PATCH 15/27] Voxel-S Lu-177 Dosimetry Tutorial --- ...sed_dosimetry_with_lu177_dose_kernel.ipynb | 459 ++++++++++++++++++ 1 file changed, 459 insertions(+) create mode 100644 docs/source/tutorials/Dosimetry_Pipeline/voxel_based_dosimetry_with_lu177_dose_kernel.ipynb diff --git a/docs/source/tutorials/Dosimetry_Pipeline/voxel_based_dosimetry_with_lu177_dose_kernel.ipynb b/docs/source/tutorials/Dosimetry_Pipeline/voxel_based_dosimetry_with_lu177_dose_kernel.ipynb new file mode 100644 index 0000000..cbc1cc9 --- /dev/null +++ b/docs/source/tutorials/Dosimetry_Pipeline/voxel_based_dosimetry_with_lu177_dose_kernel.ipynb @@ -0,0 +1,459 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1dc3882a", + "metadata": {}, + "source": [ + "# Project Setup and Initialization\n", + "Initialize new project. \n", + "\n", + "In this tutorial we will run a full dosimetry pipeline over the SNNMI Dosimetry Challenge Dataset.\n", + "\n", + "This dataset contains anonymized CT and SPECT DICOM images suitable for testing segmentation and dosimetry workflows. Data is sourced from the University of Michigan Deep Blue repository (DOI: 10.7302/864r-tb45)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa35bf4d", + "metadata": {}, + "outputs": [], + "source": [ + "from pytheranostics.data_fetchers import fetch_snmmi_dosimetry_challenge\n", + "from pytheranostics.imaging_ds.dicom_ingest import auto_setup_dosimetry_study_inventory\n", + "from pytheranostics.imaging_ds import create_studies_with_masks\n", + "from pytheranostics.segmentation import totalseg_segment, convert_masks_to_rtstruct\n", + "from pytheranostics.dosimetry import build_roi_fit_config\n", + "from pytheranostics.dosimetry.voxel_s_dosimetry import VoxelSDosimetry\n", + "from pathlib import Path\n", + "import logging\n", + "root = logging.getLogger()\n", + "root.setLevel(logging.INFO)\n", + "\n", + "# Ensure a handler exists and is set to INFO\n", + "if not root.handlers:\n", + " handler = logging.StreamHandler()\n", + " handler.setLevel(logging.INFO)\n", + " formatter = logging.Formatter(\"%(levelname)s:%(name)s:%(message)s\")\n", + " handler.setFormatter(formatter)\n", + " root.addHandler(handler)\n", + "else:\n", + " for h in root.handlers:\n", + " h.setLevel(logging.INFO)\n", + "\n", + "\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "a5bac369", + "metadata": {}, + "source": [ + "## Step 0: Initialize a New Project\n", + "\n", + "Before starting, you can use PyTheranostics' project initialization tool to set up a standardized project structure with configuration templates:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e52ae92", + "metadata": {}, + "outputs": [], + "source": [ + "from pytheranostics import init_project\n", + "\n", + "# Create a new project with all templates and standard directories\n", + "project_base = Path(\"./snmmi_dosimetry_challenge_project\")\n", + "init_project(project_base)" + ] + }, + { + "cell_type": "markdown", + "id": "d10172d8", + "metadata": {}, + "source": [ + "## Step 1: Download Example Data\n", + "\n", + "We'll use the SNMMI Dosimetry Challenge dataset from University of Michigan Deep Blue. This contains multi-timepoint SPECT/CT data and we will focus on Patient_004." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f565a978", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_snmmi_dosimetry_challenge(data_home=str(project_base))" + ] + }, + { + "cell_type": "markdown", + "id": "300d1806", + "metadata": {}, + "source": [ + "## Step 2: Segmentation using TotalSegmentator.\n", + "\n", + "Please take a look at the Total Segmentator Tutorial for more details (add link).\n", + "\n", + "### 2.1: Set up folders for where TotalSegmentator will write segmentation masks and where RT-STRUCT files will be saved." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e686b71e", + "metadata": {}, + "outputs": [], + "source": [ + "# Provide a list of ROOT folder where all CT series under it will be discovered automatically.\n", + "# In this case, as an example, we provide the path to a single CT series at the first scan of Patient_004 from the downloaded dataset.\n", + "project_data_dirs = [project_base / f\"snmmi_dose_challenge/Patient_004/SPECT_Cts/scan{i}/ct\" for i in range(1, 5)]\n", + "\n", + "# Specify paths of Where to write segmentations and RT-STRUCT outputs (will be grouped by PatientID)\n", + "totalsegmentator_output_dir = project_base / \"Segmentations\"\n", + "rtstruct_output_dir = project_base / \"rtstructs\"" + ] + }, + { + "cell_type": "markdown", + "id": "fbacd88c", + "metadata": {}, + "source": [ + "### 2.2: Run TotalSegmentator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fefba7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Run segmentation on each time point and save results in the specified output directory \n", + "seg_results = []\n", + "\n", + "for project_data_dir in project_data_dirs:\n", + "\n", + " seg_result = totalseg_segment(\n", + " root_dir=str(project_data_dir),\n", + " base_output_dir=str(totalsegmentator_output_dir),\n", + " device=\"gpu\", # Change to \"gpu\" if using an NVIDIA GPU, or \"cpu\" to run on CPU. If GPU, set number of workers to 1 and parallel=False.\n", + " parallel=False,\n", + " max_workers=1, # Number of parallel workers to use if parallel=True\n", + " )\n", + " \n", + " seg_results.append(seg_result)" + ] + }, + { + "cell_type": "markdown", + "id": "8953fb77", + "metadata": {}, + "source": [ + "### 2.3: Select organs at risk and convert binary masks to RTStruct format.\n", + "\n", + "#### Using Configuration Files\n", + "\n", + "By default, converting all 104 structures segmented by TotalSegmentator would create a very large RT-STRUCT file. Configuration files let you:\n", + "- **Filter**: Include only the organs you need (e.g., kidneys, liver, spleen)\n", + "- **Rename**: Change organ names to match your workflow conventions\n", + "- **Combine**: Merge multiple structures into one (e.g., all ribs → \"ribs\")\n", + "\n", + "\n", + "For details on configuration files, please refer to the TotalSegmentator tutorial. For this example, we will modify the `total_seg_config.json` with the following content:\n", + "\n", + "\n", + "```json\n", + "{\n", + " \"vois\": [\n", + " {\"voi_name\": \"kidney_left\", \"include\": true, \"new_name\": \"L Kidney\"},\n", + " {\"voi_name\": \"kidney_right\", \"include\": true, \"new_name\": \"R Kidney\"},\n", + " {\"voi_name\": \"liver\", \"include\": true, \"new_name\": \"Liver\"},\n", + " {\"voi_name\": \"spleen\", \"include\": true, \"new_name\": \"Spleen\"}\n", + " ],\n", + "}\n", + "```\n", + "\n", + "This configuration creates an RT-STRUCT with:\n", + "- **L Kidney**\n", + "- **R Kidney**\n", + "- **Liver**\n", + "- **Spleen**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bea8909b", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert segmentation masks to RT-STRUCT format for use in dosimetry calculations\n", + "rtstruct_results = []\n", + "for seg_result in seg_results:\n", + " rtstruct_result = convert_masks_to_rtstruct(\n", + " segmentation_base_dir=str(totalsegmentator_output_dir),\n", + " ct_series_paths=seg_result[\"ct_paths\"],\n", + " rtstruct_output_dir=str(rtstruct_output_dir ),\n", + " config_path=project_base / \"total_seg_config.json\", # Config file specifying which structures to include\n", + " )\n", + " rtstruct_results.append(rtstruct_result)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "caeef41a", + "metadata": {}, + "source": [ + "## Step 3: Auto-organize data \n", + "\n", + "See Data_Ingestion_Examples tutorial for details" + ] + }, + { + "cell_type": "markdown", + "id": "ec852c66", + "metadata": {}, + "source": [ + "### 3.0: Move freshly generated rtstruct files into the imaging data folder\n", + "\n", + " In this tutorial, SPECT and CT data lies under `project_folder/snmmi_dose_challenge/Patient_004`. The RTStruct files generated in the previous step are stored under `project_folder/rtstructs`. Please move the RTstruct files under the patient folder so that the auto-setup inventory works.\n", + "\n", + "### 3.1: Run Auto-setup / study inventory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d6c9b2f", + "metadata": {}, + "outputs": [], + "source": [ + "# Organize and extract metadata\n", + "study_info, ct_paths, spect_paths, rtstruct_files = auto_setup_dosimetry_study_inventory(\n", + " base_dir=project_base, # / \"snmmi_dose_challenge/Patient_004\", # Base directory to search for CT and SPECT series\n", + " patient_id=None, # auto-detect from DICOM headers\n", + ")\n", + "\n", + "# Quick summary\n", + "print('Patient ID:', study_info.get('patient_id'))\n", + "inj = study_info.get('injection_info', {})\n", + "print('Injection date:', inj.get('injection_date'), 'time:', inj.get('injection_time'))\n", + "print(f'CT time points: {len(ct_paths)}')\n", + "print(f'SPECT time points: {len(spect_paths)}')\n", + "print(f'RTSTRUCT files: {len(rtstruct_files)}')\n", + "\n", + "# Example: show first CT/SPECT time point folders (if present)\n", + "print('First CT tp:', ct_paths[0] if ct_paths else None)\n", + "print('First SPECT tp:', spect_paths[0] if spect_paths else None)\n", + "print('First RTSTRUCT:', rtstruct_files[0] if rtstruct_files else None)" + ] + }, + { + "cell_type": "markdown", + "id": "6afd0d68", + "metadata": {}, + "source": [ + "## Step 3: Set-up VOI Mapping and Fit/Dosimetry Parameters.\n", + "\n", + "Please visit ROI_Mapping_Tutorial for details (add link)\n", + "\n", + "### 3.1: Modify the `dosimetry_fit_defaults.json` to specify default fit parameters.\n", + "\n", + "```JSON\n", + "{\n", + " \"_description\": \"Apply bi-exponential fitting with uptake phase to all organs by default. Users can override these defaults for specific organs or ROIs by providing a custom configuration file.\",\n", + " \"organ_defaults\": {\n", + " \"fit_order\": 2,\n", + " \"with_uptake\": true,\n", + " \"param_init\": {\"A1\": 100, \"A2\": 0.01},\n", + " \"bounds\": {\"A2\": [0.004345, 1.0]},\n", + " \"fixed_parameters\": null,\n", + " \"bounds\": null,\n", + " \"washout_ratio\": null\n", + " }\n", + "}\n", + "```\n", + "\n", + "\n", + "### 3.2: Modify VOI Mapping to map names of RTStruct ROIs to standard names in pyTheranostics. Modify the key `ct_mappings` and `spect_mappings` inside `voi_mappings_config.json` to map the RTstruct names stored in segmented regions to pyTheranostics standardized names:\n", + "\n", + "```JSON\n", + "\"ct_mappings\": {\n", + " \"LKidney\": \"Kidney_Left\",\n", + " \"RKidney\": \"Kidney_Right\",\n", + " \"Liver\": \"Liver\",\n", + " \"Spleen\": \"Spleen\"\n", + " },\n", + "\n", + " \"spect_mappings\": {\n", + " \"LKidney\": \"Kidney_Left\",\n", + " \"RKidney\": \"Kidney_Right\",\n", + " \"Liver\": \"Liver\",\n", + " \"Spleen\": \"Spleen\"\n", + " }\n", + "\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "id": "4471e804", + "metadata": {}, + "source": [ + "### 3.3: Initialize Dosimetry configuration.\n", + "\n", + "In this step, we build the main configuration dictionary. It is composed of:\n", + "- ROIs fitting properties (obtained from the default `dosimetry_fit_defaults.json` or specified by the user)\n", + "- Patient ID and Injection information, that can be obtained from the `study_info` dictionary or specified by the user\n", + "- Dosimetry Level (Voxel) and method (Voxel-S)\n", + "- Since we are running voxel-S convolution method, we can specify if we scale voxel dose by density.\n", + "- Reference time point to calculate time-integrated activity at the voxel level." + ] + }, + { + "cell_type": "markdown", + "id": "c6ba09d8", + "metadata": {}, + "source": [ + "### 3.3: Load the Data and initialize configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd810f6a", + "metadata": {}, + "outputs": [], + "source": [ + "# Load data AND apply mappings in one step\n", + "longCT, longSPECT, inj, used_mappings = create_studies_with_masks(\n", + " patient_id=study_info[\"patient_id\"],\n", + " cycle_no=1,\n", + " parallel=True,\n", + " mapping_config=project_base / \"voi_mappings_config.json\",\n", + " study_info=study_info\n", + " )\n", + "\n", + "# Initilization of roi configuration \n", + "roi_config = build_roi_fit_config(longSPECT=longSPECT, config_path=project_base / \"dosimetry_fit_defaults.json\")\n", + "\n", + "# Build Dosimetry config\n", + "dosimetry_config = {\n", + " \"PatientID\": study_info[\"patient_id\"],\n", + " \"DatabaseDir\": str(project_base / \"dosimetry_database\"),\n", + " \n", + " \"VOIs\": roi_config,\n", + " \n", + " \"InjectionDate\": study_info[\"time_points\"][0][\"injection_info\"][\"injection_date\"],\n", + " \"InjectionTime\": study_info[\"time_points\"][0][\"injection_info\"][\"injection_time\"],\n", + " \"InjectedActivity\": longSPECT.meta[0].Injected_Activity_MBq,\n", + " \"Radionuclide\": longSPECT.meta[0].Radionuclide,\n", + " \"PatientWeight_g\": study_info[\"time_points\"][0][\"injection_info\"][\"patient_weight_g\"],\n", + " \n", + " \n", + " \"Level\": \"Voxel\",\n", + " \"Method\": \"Voxel-S-value\",\n", + " \"ScaleDoseByDensity\": False,\n", + " \n", + " \"ReferenceTimePoint\": 0\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "827252b6", + "metadata": {}, + "source": [ + "### 3.4: Initialize Dosimetry Calculator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e87cc410", + "metadata": {}, + "outputs": [], + "source": [ + "Dosimetry = VoxelSDosimetry(config=dosimetry_config, nm_data=longSPECT, ct_data=longCT)" + ] + }, + { + "cell_type": "markdown", + "id": "84663d53", + "metadata": {}, + "source": [ + "### 3.5 Compute Dose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1fc71a4", + "metadata": {}, + "outputs": [], + "source": [ + "Dosimetry.compute_dose()" + ] + }, + { + "cell_type": "markdown", + "id": "4a2607f1", + "metadata": {}, + "source": [ + "### 3.6 Review Results\n", + "\n", + "Time-activity data, time-integrated activity (residence times) at the region level at stored under `Dosimetry.results`. Mean dose per region (derived from Voxelized dose map) is summaried in `Dosimetry.df_ad`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7788cdca", + "metadata": {}, + "outputs": [], + "source": [ + "# View Time-Activity data, parameters of fit, etc.\n", + "Dosimetry.results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "251b81a6", + "metadata": {}, + "outputs": [], + "source": [ + "# View Average Dose\n", + "Dosimetry.df_ad" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".pyThera-DBG", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 644b9880f3c57c82326e3ceaef6462b4a233d7c2 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:12:53 -0700 Subject: [PATCH 16/27] Update project toml --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index ec73121..52fc43f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -163,6 +163,7 @@ profile = "black" markers = [ "smoke: marks tests as smoke tests (quick functionality checks)", "slow: marks tests as slow (heavy computation or large data)", + "integration: marks tests as integration tests spanning multiple subsystems", ] filterwarnings = [ "ignore:.*SwigPy.*:DeprecationWarning", From 425a4adb5db308ddc17b1962094c7f204b9f5df4 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:14:04 -0700 Subject: [PATCH 17/27] Update gitignore --- .gitignore | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 8ab39ee..b53cb86 100644 --- a/.gitignore +++ b/.gitignore @@ -129,6 +129,8 @@ dmypy.json # Pyre type checker .pyre/ +# VS Code settings +.vscode # Other Notebooks @@ -137,4 +139,5 @@ pytheranostics/local pytheranostics/.vscode pytheranostics/data/s-values -pytheranostics/data/ICRP_phantom_masses \ No newline at end of file +pytheranostics/data/ICRP_phantom_masses +pytheranostics/data/voxel_kernels \ No newline at end of file From df5e20acc79e9da17f478a6a7526f799aad51aa7 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:21:00 -0700 Subject: [PATCH 18/27] pre-commit hooks updates (flake8, black, etc.) --- pytheranostics/dosimetry/base_dosimetry.py | 26 ++-- pytheranostics/dosimetry/config.py | 2 +- pytheranostics/dosimetry/voxel_s_dosimetry.py | 23 ++-- pytheranostics/imaging_ds/cycle_loader.py | 4 +- .../imaging_ds/longitudinal_study.py | 12 +- pytheranostics/imaging_tools/tools.py | 22 +++- tests/test_voxel_dosimetry_validation.py | 121 ++++++++++-------- 7 files changed, 124 insertions(+), 86 deletions(-) diff --git a/pytheranostics/dosimetry/base_dosimetry.py b/pytheranostics/dosimetry/base_dosimetry.py index cf056c0..36e33cc 100644 --- a/pytheranostics/dosimetry/base_dosimetry.py +++ b/pytheranostics/dosimetry/base_dosimetry.py @@ -87,10 +87,14 @@ def __init__( self.toMBq = 1e-6 # Factor to scale activity from Bq to MBq # Store data - self.patient_id = config["PatientID"] if "PatientID" in config else "UnknownPatient" + self.patient_id = ( + config["PatientID"] if "PatientID" in config else "UnknownPatient" + ) self.cycle = config["Cycle"] if "Cycle" in config else 1 - self.db_dir = Path(config["DatabaseDir"]) if "DatabaseDir" in config else Path("./") - + self.db_dir = ( + Path(config["DatabaseDir"]) if "DatabaseDir" in config else Path("./") + ) + self.check_mandatory_fields() self.check_patient_in_db() # TODO: Traceability/database? @@ -246,7 +250,6 @@ def check_mandatory_fields(self) -> None: "param_init": None, } - if "Organ" in self.config["Level"]: if "WholeBody" not in self.config["VOIs"]: if "No" in self.config["OrganLevel"]["AdditionalOptions"]["WholeBody"]: @@ -270,7 +273,8 @@ def initialize(self) -> pandas.DataFrame: tmp_results: Dict[str, List[float]] = { roi_name: [] for roi_name in self.nm_data.masks[0].keys() - if roi_name in self.config["VOIs"] or roi_name in ["WholeBody", "RemainderOfBody"] + if roi_name in self.config["VOIs"] + or roi_name in ["WholeBody", "RemainderOfBody"] } cols: List[str] = ["Time_hr", "Volume_CT_mL", "Activity_MBq", "Density_HU"] @@ -281,7 +285,7 @@ def initialize(self) -> pandas.DataFrame: self.normalize_time_to_injection(time_id=time_id) for roi_name in tmp_results.keys(): - + # Time (relative to time of injection, in hours) tmp_results[roi_name].append( [self.nm_data.meta[time_id].HoursAfterInjection for time_id in time_ids] @@ -427,10 +431,12 @@ def compute_tia(self) -> None: } for region, region_data in self.results.iterrows(): - - if type(region) != str: - raise TypeError(f"Region names should be strings. Found {type(region)} instead.") - + + if not isinstance(region, str): + raise TypeError( + f"Region names should be strings. Found {type(region)} instead." + ) + fit_results = self.smart_fit_selection( region_data=region_data, region=region ) diff --git a/pytheranostics/dosimetry/config.py b/pytheranostics/dosimetry/config.py index b2d0442..030c9e3 100644 --- a/pytheranostics/dosimetry/config.py +++ b/pytheranostics/dosimetry/config.py @@ -158,7 +158,7 @@ def build_roi_fit_config( all_masks = set() for tp_masks in getattr(longSPECT, "masks", {}).values(): all_masks.update(tp_masks.keys()) - + # Add WholeBody and RemainderOfBody if they are not available in masks, as they will be needed for dosimetry (and will be generated by default if not present) if "WholeBody" not in all_masks: all_masks.add("WholeBody") diff --git a/pytheranostics/dosimetry/voxel_s_dosimetry.py b/pytheranostics/dosimetry/voxel_s_dosimetry.py index 7765c14..622f1f6 100644 --- a/pytheranostics/dosimetry/voxel_s_dosimetry.py +++ b/pytheranostics/dosimetry/voxel_s_dosimetry.py @@ -3,7 +3,6 @@ import logging import os import shutil -from pathlib import Path from typing import Any, Dict, Optional, Tuple import numpy @@ -74,10 +73,12 @@ def compute_voxel_tia(self) -> None: masks = numpy.zeros_like(tia_map, dtype=numpy.int8) for region, region_data in self.results.iterrows(): - + # Type check: - if type(region) != str: - raise TypeError(f"Region names should be strings. Found {type(region)} instead.") + if not isinstance(region, str): + raise TypeError( + f"Region names should be strings. Found {type(region)} instead." + ) if region == "WholeBody": continue # We do not want to double count voxels! @@ -154,7 +155,11 @@ def apply_voxel_s(self) -> None: ) kernel_ref_image = self._build_reference_image_with_spacing( ref_image=tia_ref_image, - spacing_mm=(dose_kernel.voxel_size_mm, dose_kernel.voxel_size_mm, dose_kernel.voxel_size_mm), + spacing_mm=( + dose_kernel.voxel_size_mm, + dose_kernel.voxel_size_mm, + dose_kernel.voxel_size_mm, + ), ) tia_kernel_grid = self._resample_tia_to_target_grid(target_img=kernel_ref_image) @@ -179,7 +184,9 @@ def apply_voxel_s(self) -> None: array=numpy.transpose(kernel_dose_array, axes=(2, 0, 1)), ref_image=kernel_ref_image, ) - logger.info("Resampling dose map from kernel grid to %s output grid.", output_grid) + logger.info( + "Resampling dose map from kernel grid to %s output grid.", output_grid + ) dose_map_array = numpy.transpose( SimpleITK.GetArrayFromImage( resample_to_target( @@ -226,9 +233,7 @@ def _build_reference_image_with_spacing( max( 1, int( - round( - original_size[idx] * original_spacing[idx] / spacing_mm[idx] - ) + round(original_size[idx] * original_spacing[idx] / spacing_mm[idx]) ), ) for idx in range(3) diff --git a/pytheranostics/imaging_ds/cycle_loader.py b/pytheranostics/imaging_ds/cycle_loader.py index e094162..f98a995 100644 --- a/pytheranostics/imaging_ds/cycle_loader.py +++ b/pytheranostics/imaging_ds/cycle_loader.py @@ -513,7 +513,7 @@ def create_studies_with_masks( raise RuntimeError( f"No SPECT image available for timepoint {time_id} to resample NM masks" ) - + ct_masks, nm_masks = load_and_resample_RT_to_target( ref_dicom_ct_dir=str(ct_dir), rt_struct_file=str(rt_file), @@ -575,7 +575,7 @@ def _is_valid_target(name: str) -> bool: if final_spect_mapping is not None: for k in nm_masks.keys(): dst = final_spect_mapping.get(k) - + if dst is not None and _is_valid_target(dst): spect_map_valid[k] = dst else: diff --git a/pytheranostics/imaging_ds/longitudinal_study.py b/pytheranostics/imaging_ds/longitudinal_study.py index e8ff1e0..e078b8a 100644 --- a/pytheranostics/imaging_ds/longitudinal_study.py +++ b/pytheranostics/imaging_ds/longitudinal_study.py @@ -800,11 +800,13 @@ def activity_in(self, region: str, time_id: int) -> float: raise AssertionError( "Can't compute activity if the image data does not represent the distribution of a radionuclide" ) - return float(numpy.sum( - self.masks[time_id][region] - * self.array_at(time_id=time_id) - * self.voxel_volume(time_id=time_id) - )) + return float( + numpy.sum( + self.masks[time_id][region] + * self.array_at(time_id=time_id) + * self.voxel_volume(time_id=time_id) + ) + ) def density_of(self, region: str, time_id: int) -> float: """Return the mean density of region of interest, in HU. diff --git a/pytheranostics/imaging_tools/tools.py b/pytheranostics/imaging_tools/tools.py index bccca61..8988ee8 100644 --- a/pytheranostics/imaging_tools/tools.py +++ b/pytheranostics/imaging_tools/tools.py @@ -293,7 +293,9 @@ def apply_qspect_dcm_scaling( f"Found more than 1 .dcm file inside {path_dir.name}, not sure which one is the right SPECT." ) - image_array = numpy.squeeze(SimpleITK.GetArrayFromImage(image)).astype(numpy.float32) + image_array = numpy.squeeze(SimpleITK.GetArrayFromImage(image)).astype( + numpy.float32 + ) if scale_factor is not None: slope = float(scale_factor[0]) @@ -322,13 +324,21 @@ def apply_qspect_dcm_scaling( ) decay_correction = str(getattr(dcm_data, "DecayCorrection", "") or "").upper() - logger.info("DICOM DecayCorrection for %s image is '%s'.", modality, decay_correction or "MISSING") + logger.info( + "DICOM DecayCorrection for %s image is '%s'.", + modality, + decay_correction or "MISSING", + ) if decay_correction == "START": - logger.info("No additional decay normalization is needed because the image is already referenced to acquisition start.") + logger.info( + "No additional decay normalization is needed because the image is already referenced to acquisition start." + ) elif decay_correction == "ADMIN": acquisition_dt = _get_acquisition_start_datetime(dcm_data) - admin_dt, half_life_seconds = _get_radiopharm_admin_datetime_and_half_life(dcm_data) + admin_dt, half_life_seconds = _get_radiopharm_admin_datetime_and_half_life( + dcm_data + ) if acquisition_dt is None: logger.warning( @@ -352,7 +362,9 @@ def apply_qspect_dcm_scaling( admin_dt.isoformat(), ) else: - decay_factor = math.exp(-math.log(2.0) * delta_seconds / half_life_seconds) + decay_factor = math.exp( + -math.log(2.0) * delta_seconds / half_life_seconds + ) logger.info( "Converting %s image from ADMIN to START decay reference using administration time %s, acquisition time %s, half-life %.6g s, elapsed time %.3f s, factor %.9g.", modality, diff --git a/tests/test_voxel_dosimetry_validation.py b/tests/test_voxel_dosimetry_validation.py index 75c73c2..e421e2f 100644 --- a/tests/test_voxel_dosimetry_validation.py +++ b/tests/test_voxel_dosimetry_validation.py @@ -20,23 +20,23 @@ from pytheranostics.imaging_ds.dicom_ingest import auto_setup_dosimetry_study_inventory _VALIDATION_ASSETS: Final[Path] = ( - Path(__file__).resolve().parent / 'data' / 'voxel_dosimetry_validation' + Path(__file__).resolve().parent / "data" / "voxel_dosimetry_validation" ) -_RTSTRUCTS_DIR: Final[Path] = _VALIDATION_ASSETS / 'rtstructs' -_EXPECTED_RESULTS_CSV: Final[Path] = _VALIDATION_ASSETS / 'expected_results.csv' -_EXPECTED_DF_AD_CSV: Final[Path] = _VALIDATION_ASSETS / 'expected_df_ad.csv' +_RTSTRUCTS_DIR: Final[Path] = _VALIDATION_ASSETS / "rtstructs" +_EXPECTED_RESULTS_CSV: Final[Path] = _VALIDATION_ASSETS / "expected_results.csv" +_EXPECTED_DF_AD_CSV: Final[Path] = _VALIDATION_ASSETS / "expected_df_ad.csv" _OPTIONAL_CONFIG_FILES: Final[tuple[str, ...]] = ( - 'voi_mappings_config.json', - 'dosimetry_fit_defaults.json', + "voi_mappings_config.json", + "dosimetry_fit_defaults.json", ) _LIST_LIKE_RESULTS_COLUMNS: Final[set[str]] = { - 'Time_hr', - 'Volume_CT_mL', - 'Activity_MBq', - 'Density_HU', - 'Fit_params', - 'R_squared_AIC', - 'Lambda_eff', + "Time_hr", + "Volume_CT_mL", + "Activity_MBq", + "Density_HU", + "Fit_params", + "R_squared_AIC", + "Lambda_eff", } @@ -48,9 +48,9 @@ def _skip_if_validation_assets_missing() -> None: ] if missing: pytest.skip( - 'Voxel dosimetry validation assets are not available. Missing: ' - + ', '.join(missing) - + f'. Populate {_VALIDATION_ASSETS} to enable this test.' + "Voxel dosimetry validation assets are not available. Missing: " + + ", ".join(missing) + + f". Populate {_VALIDATION_ASSETS} to enable this test." ) @@ -62,7 +62,7 @@ def _copy_optional_validation_configs(project_base: Path) -> None: def _copy_rtstruct_assets(project_base: Path) -> None: - target_dir = project_base / 'rtstructs' + target_dir = project_base / "rtstructs" shutil.copytree(_RTSTRUCTS_DIR, target_dir, dirs_exist_ok=True) @@ -75,7 +75,7 @@ def _parse_numeric_list_cell(value: object) -> object: return value stripped = value.strip() - if not (stripped.startswith('[') and stripped.endswith(']')): + if not (stripped.startswith("[") and stripped.endswith("]")): return value inner = stripped[1:-1].strip() @@ -83,7 +83,7 @@ def _parse_numeric_list_cell(value: object) -> object: return [] cleaned = re.sub(r"np\.float64\(([^()]*)\)", r"\1", inner) - return [float(item.strip()) for item in cleaned.split(',')] + return [float(item.strip()) for item in cleaned.split(",")] def _normalize_sequence_cell(value: object) -> object: @@ -136,8 +136,8 @@ def _prepare_frame_for_comparison( missing_columns = [col for col in expected.columns if col not in actual.columns] if missing_columns: raise AssertionError( - 'Actual dataframe is missing expected columns: ' - + ', '.join(missing_columns) + "Actual dataframe is missing expected columns: " + + ", ".join(missing_columns) ) actual_prepared = actual.loc[:, list(expected.columns)].copy() @@ -146,10 +146,13 @@ def _prepare_frame_for_comparison( actual_prepared.index = actual_prepared.index.map(str) expected_prepared.index = expected_prepared.index.map(str) - missing_index = [idx for idx in expected_prepared.index if idx not in actual_prepared.index] + missing_index = [ + idx for idx in expected_prepared.index if idx not in actual_prepared.index + ] if missing_index: raise AssertionError( - 'Actual dataframe is missing expected index entries: ' + ', '.join(missing_index) + "Actual dataframe is missing expected index entries: " + + ", ".join(missing_index) ) actual_prepared = actual_prepared.loc[list(expected_prepared.index)] @@ -172,61 +175,67 @@ def _prepare_frame_for_comparison( def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: _skip_if_validation_assets_missing() - project_base = tmp_path / 'snmmi_dosimetry_validation_project' + project_base = tmp_path / "snmmi_dosimetry_validation_project" init_project(project_base) _copy_optional_validation_configs(project_base) try: fetch_snmmi_dosimetry_challenge(data_home=str(project_base)) except RuntimeError as exc: - pytest.skip(f'SNMMI dosimetry dataset could not be fetched: {exc}') + pytest.skip(f"SNMMI dosimetry dataset could not be fetched: {exc}") _copy_rtstruct_assets(project_base) - study_info, ct_paths, spect_paths, rtstruct_files = auto_setup_dosimetry_study_inventory( - base_dir=project_base, - patient_id=None, + study_info, ct_paths, spect_paths, rtstruct_files = ( + auto_setup_dosimetry_study_inventory( + base_dir=project_base, + patient_id=None, + ) ) - assert study_info.get('patient_id') is not None - assert ct_paths, 'No CT timepoints were discovered by the validation pipeline.' - assert spect_paths, 'No SPECT timepoints were discovered by the validation pipeline.' - assert rtstruct_files, 'No RTSTRUCT files were discovered by the validation pipeline.' + assert study_info.get("patient_id") is not None + assert ct_paths, "No CT timepoints were discovered by the validation pipeline." + assert ( + spect_paths + ), "No SPECT timepoints were discovered by the validation pipeline." + assert ( + rtstruct_files + ), "No RTSTRUCT files were discovered by the validation pipeline." long_ct, long_spect, _, _ = create_studies_with_masks( - patient_id=study_info['patient_id'], + patient_id=study_info["patient_id"], cycle_no=1, parallel=True, - mapping_config=project_base / 'voi_mappings_config.json', + mapping_config=project_base / "voi_mappings_config.json", study_info=study_info, ) roi_config = build_roi_fit_config( longSPECT=long_spect, - config_path=project_base / 'dosimetry_fit_defaults.json', + config_path=project_base / "dosimetry_fit_defaults.json", ) - first_tp_injection = study_info['time_points'][0]['injection_info'] - database_dir = project_base / 'dosimetry_database' - results_dir = project_base / 'results' + first_tp_injection = study_info["time_points"][0]["injection_info"] + database_dir = project_base / "dosimetry_database" + results_dir = project_base / "results" database_dir.mkdir(parents=True, exist_ok=True) results_dir.mkdir(parents=True, exist_ok=True) dosimetry_config = { - 'PatientID': study_info['patient_id'], - 'Cycle': 1, - 'DatabaseDir': str(database_dir), - 'results_path': str(results_dir), - 'VOIs': roi_config, - 'InjectionDate': first_tp_injection['injection_date'], - 'InjectionTime': first_tp_injection['injection_time'], - 'InjectedActivity': long_spect.meta[0].Injected_Activity_MBq, - 'Radionuclide': long_spect.meta[0].Radionuclide, - 'PatientWeight_g': first_tp_injection['patient_weight_g'], - 'Level': 'Voxel', - 'Method': 'Voxel-S-value', - 'ScaleDoseByDensity': False, - 'ReferenceTimePoint': 0, + "PatientID": study_info["patient_id"], + "Cycle": 1, + "DatabaseDir": str(database_dir), + "results_path": str(results_dir), + "VOIs": roi_config, + "InjectionDate": first_tp_injection["injection_date"], + "InjectionTime": first_tp_injection["injection_time"], + "InjectedActivity": long_spect.meta[0].Injected_Activity_MBq, + "Radionuclide": long_spect.meta[0].Radionuclide, + "PatientWeight_g": first_tp_injection["patient_weight_g"], + "Level": "Voxel", + "Method": "Voxel-S-value", + "ScaleDoseByDensity": False, + "ReferenceTimePoint": 0, } dosimetry = VoxelSDosimetry( @@ -253,8 +262,12 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: atol=1e-6, ) assert_frame_equal( - actual_results.drop(columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(actual_results.columns))), - expected_results.drop(columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(expected_results.columns))), + actual_results.drop( + columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(actual_results.columns)) + ), + expected_results.drop( + columns=list(_LIST_LIKE_RESULTS_COLUMNS & set(expected_results.columns)) + ), check_dtype=False, rtol=1e-4, atol=1e-6, From 7a2c4169199fd42d6147a849766852192b96e6f3 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:21:49 -0700 Subject: [PATCH 19/27] Fix docstyle --- pytheranostics/imaging_ds/dicom_ingest.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/pytheranostics/imaging_ds/dicom_ingest.py b/pytheranostics/imaging_ds/dicom_ingest.py index 5a25861..5f2f69e 100644 --- a/pytheranostics/imaging_ds/dicom_ingest.py +++ b/pytheranostics/imaging_ds/dicom_ingest.py @@ -17,9 +17,7 @@ def _split_dicom_datetime(dt_val: Optional[str]) -> Tuple[Optional[str], Optional[str]]: - """ - Split a DICOM DT value into date (YYYYMMDD) and time (HHMMSS). - """ + """Split a DICOM DT value into date (YYYYMMDD) and time (HHMMSS).""" if not dt_val: return None, None val = str(dt_val).split(".")[0] From 1356c5de61e7b30569016c76d2cf84e7d569b46a Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:29:17 -0700 Subject: [PATCH 20/27] Fix test dvk --- pytheranostics/dosimetry/dvk.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index 1a193c2..c8b8ea7 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -191,7 +191,7 @@ def _ensure_kernel_files_available( kernel_dir, ) - _download_kernels_from_zenodo(isotope=isotope, kernel_dir=kernel_dir) + _download_kernels_from_zenodo(isotope, kernel_dir) kernels = _discover_kernel_files(kernel_dir=kernel_dir, isotope=isotope) if not kernels: raise NotImplementedError( From 5cbfbb981286411431842f46e1d24382af19adaf Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Fri, 27 Mar 2026 13:36:32 -0700 Subject: [PATCH 21/27] Update with numpy docstrings --- pytheranostics/dosimetry/dvk.py | 269 ++++++++++++++++-- pytheranostics/dosimetry/voxel_s_dosimetry.py | 103 ++++++- 2 files changed, 337 insertions(+), 35 deletions(-) diff --git a/pytheranostics/dosimetry/dvk.py b/pytheranostics/dosimetry/dvk.py index c8b8ea7..d7bb4ef 100644 --- a/pytheranostics/dosimetry/dvk.py +++ b/pytheranostics/dosimetry/dvk.py @@ -36,7 +36,19 @@ @dataclass(frozen=True) class KernelMetadata: - """Metadata parsed from a dose kernel CSV filename.""" + """Metadata parsed from a dose-kernel CSV filename. + + Attributes + ---------- + path : Path + Path to the CSV file on disk. + isotope : str + Canonical isotope label, for example ``"Lu177"``. + voxel_size_mm : float + Isotropic voxel size represented by the kernel, in millimeters. + matrix_size : int + Edge length of the cubic full kernel matrix. + """ path: Path isotope: str @@ -45,7 +57,23 @@ class KernelMetadata: def _split_isotope(isotope: str) -> Tuple[str, str]: - """Return isotope mass number and symbol from either `Lu177` or `177Lu`.""" + """Split an isotope label into mass number and element symbol. + + Parameters + ---------- + isotope : str + Isotope label formatted as ``"Lu177"`` or ``"177Lu"``. + + Returns + ------- + tuple of str + Mass number followed by the element symbol. + + Raises + ------ + ValueError + If ``isotope`` does not match one of the supported label formats. + """ match = re.fullmatch(r"(?P[A-Za-z]+)(?P\d+)", isotope) if match is None: match = re.fullmatch(r"(?P\d+)(?P[A-Za-z]+)", isotope) @@ -60,19 +88,54 @@ def _split_isotope(isotope: str) -> Tuple[str, str]: def _canonical_isotope(isotope: str) -> str: - """Convert an isotope string to a canonical representation for comparisons.""" + """Convert an isotope label to the canonical comparison format. + + Parameters + ---------- + isotope : str + Isotope label formatted as ``"Lu177"`` or ``"177Lu"``. + + Returns + ------- + str + Canonical label with the element symbol first, for example ``"Lu177"``. + """ mass, symbol = _split_isotope(isotope) return f"{symbol.capitalize()}{mass}" def _zenodo_isotope_name(isotope: str) -> str: - """Convert an isotope string to Zenodo's archive naming convention.""" + """Convert an isotope label to Zenodo's archive naming convention. + + Parameters + ---------- + isotope : str + Isotope label formatted as ``"Lu177"`` or ``"177Lu"``. + + Returns + ------- + str + Archive label with the mass number first, for example ``"177Lu"``. + """ mass, symbol = _split_isotope(isotope) return f"{mass}{symbol.capitalize()}" def _discover_kernel_files(kernel_dir: Path, isotope: str) -> List[KernelMetadata]: - """Return kernel CSVs matching the requested isotope.""" + """Discover local kernel CSV files matching an isotope. + + Parameters + ---------- + kernel_dir : Path + Root directory searched recursively for kernel CSV files. + isotope : str + Requested isotope label. + + Returns + ------- + list of KernelMetadata + Parsed metadata for matching kernel files. + """ requested_isotope = _canonical_isotope(isotope) kernels: List[KernelMetadata] = [] @@ -100,7 +163,25 @@ def _discover_kernel_files(kernel_dir: Path, isotope: str) -> List[KernelMetadat def _select_closest_kernel( requested_voxel_size_mm: float, available_kernels: Sequence[KernelMetadata] ) -> KernelMetadata: - """Return the kernel whose voxel size is closest to the requested size.""" + """Select the available kernel whose voxel size is closest to the request. + + Parameters + ---------- + requested_voxel_size_mm : float + Requested isotropic voxel size in millimeters. + available_kernels : sequence of KernelMetadata + Candidate kernels for the isotope of interest. + + Returns + ------- + KernelMetadata + Metadata for the closest matching kernel. + + Raises + ------ + ValueError + If ``available_kernels`` is empty. + """ if not available_kernels: raise ValueError("No kernels were provided for voxel-size selection.") @@ -122,7 +203,24 @@ def _select_closest_kernel( def _download_kernels_from_zenodo(isotope: str, kernel_dir: Path) -> None: - """Download and extract dose-kernel CSVs for the requested isotope.""" + """Download and extract dose-kernel CSV files for an isotope. + + Parameters + ---------- + isotope : str + Requested isotope label. + kernel_dir : Path + Destination directory where the downloaded archive is extracted. + + Raises + ------ + NotImplementedError + If no Zenodo archive exists for the requested isotope. + FileNotFoundError + If the downloaded archive does not contain any CSV kernel files. + urllib.error.URLError + If the archive download fails for a network-related reason. + """ zenodo_name = _zenodo_isotope_name(isotope) url = ( f"https://zenodo.org/records/{_ZENODO_RECORD_ID}/files/" @@ -170,7 +268,26 @@ def _download_kernels_from_zenodo(isotope: str, kernel_dir: Path) -> None: def _ensure_kernel_files_available( kernel_dir: Path, isotope: str ) -> List[KernelMetadata]: - """Ensure that kernel CSVs for the requested isotope are available locally.""" + """Ensure that kernel CSV files for an isotope are available locally. + + Parameters + ---------- + kernel_dir : Path + Root directory containing local kernel files. + isotope : str + Requested isotope label. + + Returns + ------- + list of KernelMetadata + Available kernels for the requested isotope after local discovery and any + required download. + + Raises + ------ + NotImplementedError + If matching kernels are still unavailable after the download attempt. + """ kernels = _discover_kernel_files(kernel_dir=kernel_dir, isotope=isotope) if kernels: return kernels @@ -202,7 +319,24 @@ def _ensure_kernel_files_available( def _load_octant_kernel(csv_path: Path) -> numpy.ndarray: - """Load the positive octant stored in a kernel CSV.""" + """Load a stored positive octant from a kernel CSV file. + + Parameters + ---------- + csv_path : Path + Path to a kernel CSV file using the stacked-square Zenodo layout. + + Returns + ------- + numpy.ndarray + Three-dimensional positive octant of the kernel. + + Raises + ------ + ValueError + If the CSV file is empty, malformed, or cannot be reshaped into stacked + square sections. + """ with csv_path.open("r", encoding="utf-8", newline="") as file_obj: rows = [ row for row in csv.reader(file_obj) if any(cell.strip() for cell in row) @@ -238,7 +372,20 @@ def _load_octant_kernel(csv_path: Path) -> numpy.ndarray: def _mirror_axis_from_center(half_kernel: numpy.ndarray, axis: int) -> numpy.ndarray: - """Mirror a kernel axis where index 0 is the center voxel.""" + """Mirror a kernel axis around the center voxel. + + Parameters + ---------- + half_kernel : numpy.ndarray + Kernel array where index 0 along ``axis`` corresponds to the center voxel. + axis : int + Axis to mirror. + + Returns + ------- + numpy.ndarray + Kernel with the requested axis mirrored onto the negative side. + """ slicer = [slice(None)] * half_kernel.ndim slicer[axis] = slice(1, None) positive_side = half_kernel[tuple(slicer)] @@ -247,7 +394,19 @@ def _mirror_axis_from_center(half_kernel: numpy.ndarray, axis: int) -> numpy.nda def _build_full_kernel_from_octant(octant: numpy.ndarray) -> numpy.ndarray: - """Expand the stored positive octant into a full 3-D kernel.""" + """Expand a stored positive octant into a full three-dimensional kernel. + + Parameters + ---------- + octant : numpy.ndarray + Positive octant whose index 0 corresponds to the kernel center along each + axis. + + Returns + ------- + numpy.ndarray + Full symmetric three-dimensional kernel. + """ kernel = octant for axis in range(3): kernel = _mirror_axis_from_center(kernel, axis=axis) @@ -255,7 +414,25 @@ def _build_full_kernel_from_octant(octant: numpy.ndarray) -> numpy.ndarray: def _odd_voxel_count_for_physical_size(size_mm: float, voxel_size_mm: float) -> int: - """Return the odd voxel count closest to a requested physical crop size.""" + """Return the nearest odd voxel count for a requested physical size. + + Parameters + ---------- + size_mm : float + Requested physical size in millimeters. + voxel_size_mm : float + Isotropic voxel size in millimeters. + + Returns + ------- + int + Odd voxel count closest to the requested physical extent. + + Raises + ------ + ValueError + If ``size_mm`` is not strictly positive. + """ if size_mm <= 0: raise ValueError("Crop size must be positive when cropping is enabled.") @@ -274,7 +451,23 @@ def _odd_voxel_count_for_physical_size(size_mm: float, voxel_size_mm: float) -> def _crop_kernel_around_center( kernel: numpy.ndarray, voxel_size_mm: float, crop_size_mm: Optional[float] ) -> numpy.ndarray: - """Crop a kernel symmetrically around its center voxel.""" + """Crop a kernel symmetrically around its center voxel. + + Parameters + ---------- + kernel : numpy.ndarray + Full three-dimensional dose kernel. + voxel_size_mm : float + Isotropic voxel size represented by ``kernel``. + crop_size_mm : float, optional + Requested physical crop size in millimeters. If ``None``, no cropping is + applied. + + Returns + ------- + numpy.ndarray + Cropped kernel, or the original kernel when no crop is needed. + """ if crop_size_mm is None: return kernel @@ -290,7 +483,29 @@ def _crop_kernel_around_center( class DoseVoxelKernel: - """Dose Voxel Kernel for convolution-based dosimetry calculations.""" + """Dose voxel kernel used for convolution-based dosimetry calculations. + + Parameters + ---------- + isotope : str + Requested isotope label. + voxel_size_mm : float + Requested isotropic voxel size in millimeters. + crop_kernel_size_mm : float, optional + Physical crop size of the cubic kernel in millimeters. Pass ``None`` to + keep the full kernel. + + Attributes + ---------- + kernel : numpy.ndarray + Loaded full or cropped dose kernel. + voxel_size_mm : float + Voxel size of the selected kernel in millimeters. + matrix_size : int + Matrix size of the selected full kernel before optional cropping. + isotope : str + Canonical isotope label of the selected kernel. + """ def __init__( self, @@ -298,14 +513,24 @@ def __init__( voxel_size_mm: float, crop_kernel_size_mm: Optional[float] = _DEFAULT_CROP_SIZE_MM, ) -> None: - """Initialize the DoseVoxelKernel. - - Args - ---- - isotope (str): The isotope name (e.g., 'Lu177'). - voxel_size_mm (float): Requested voxel size in millimeters. - crop_kernel_size_mm (float | None): Physical cubic crop size in mm. - Use ``None`` to disable cropping. + """Initialize a dose voxel kernel from local or downloaded CSV files. + + Parameters + ---------- + isotope : str + Requested isotope label, for example ``"Lu177"``. + voxel_size_mm : float + Requested isotropic voxel size in millimeters. + crop_kernel_size_mm : float, optional + Physical cubic crop size in millimeters. Pass ``None`` to disable + cropping. + + Raises + ------ + ValueError + If the reconstructed kernel shape does not match the filename metadata. + NotImplementedError + If no kernel files can be found or downloaded for ``isotope``. """ available_kernels = _ensure_kernel_files_available(_DATA_DIR, isotope) selected_kernel = _select_closest_kernel( diff --git a/pytheranostics/dosimetry/voxel_s_dosimetry.py b/pytheranostics/dosimetry/voxel_s_dosimetry.py index 622f1f6..27fb2e6 100644 --- a/pytheranostics/dosimetry/voxel_s_dosimetry.py +++ b/pytheranostics/dosimetry/voxel_s_dosimetry.py @@ -21,10 +21,10 @@ class VoxelSDosimetry(BaseDosimetry): - """Voxel S Dosimetry class. + """Voxel S-value dosimetry workflow. - Computes parameters of fit for time activity curves at the region (organ/lesion) level, - and apply them at the voxel level for voxels belonging to user-defined regions. + Computes time-activity fits at the region level and propagates the resulting + kinetics to voxels belonging to user-defined regions. """ def __init__( @@ -128,7 +128,12 @@ def compute_voxel_tia(self) -> None: return None def apply_voxel_s(self) -> None: - """Apply convolution over TIA map.""" + """Convolve the voxel TIA map with the selected dose kernel. + + Resamples the TIA map to the kernel grid, optionally applies local + density-based dose scaling using CT, and stores the resulting dose map on + the configured NM or CT output grid. + """ ref_time_id = self.config["ReferenceTimePoint"] output_grid = str(self.config.get("VoxelSOutputGrid", "NM")).upper() logger.info("Applying voxel-S dosimetry using %s output grid.", output_grid) @@ -226,7 +231,21 @@ def apply_voxel_s(self) -> None: def _build_reference_image_with_spacing( ref_image: SimpleITK.Image, spacing_mm: Tuple[float, float, float] ) -> SimpleITK.Image: - """Create a blank reference image using a new spacing on the same physical frame.""" + """Create a blank reference image on the same physical frame. + + Parameters + ---------- + ref_image : SimpleITK.Image + Source image providing origin, direction, and physical extent. + spacing_mm : tuple of float + Requested output spacing in millimeters. + + Returns + ------- + SimpleITK.Image + Blank image with the requested spacing and a size adjusted to preserve + the source physical coverage. + """ original_spacing = ref_image.GetSpacing()[:3] original_size = ref_image.GetSize()[:3] new_size = [ @@ -246,7 +265,20 @@ def _build_reference_image_with_spacing( @staticmethod def _scalar_voxel_size_mm(image: SimpleITK.Image, image_name: str) -> float: - """Return a scalar voxel size for isotropic-kernel selection.""" + """Return a scalar voxel size for isotropic kernel selection. + + Parameters + ---------- + image : SimpleITK.Image + Image whose spacing is used for kernel selection. + image_name : str + Label used in warning messages when the image spacing is anisotropic. + + Returns + ------- + float + Mean voxel spacing in millimeters across the first three axes. + """ spacing = tuple(float(value) for value in image.GetSpacing()[:3]) mean_spacing = float(numpy.mean(spacing)) if not numpy.allclose(spacing, mean_spacing, atol=0.1): @@ -262,7 +294,18 @@ def _scalar_voxel_size_mm(image: SimpleITK.Image, image_name: str) -> float: def _resample_tia_to_target_grid( self, target_img: SimpleITK.Image ) -> numpy.ndarray: - """Resample TIA totals per voxel through a temporary density representation.""" + """Resample the TIA map to a target grid while preserving total activity. + + Parameters + ---------- + target_img : SimpleITK.Image + Target image defining the output voxel grid. + + Returns + ------- + numpy.ndarray + Resampled TIA map on the target grid, expressed in MBq h per voxel. + """ logger.info( "Resampling TIA map to kernel grid using voxel-volume normalization to preserve total activity." ) @@ -301,14 +344,43 @@ def _resample_tia_to_target_grid( @staticmethod def _voxel_volume_ml(image: SimpleITK.Image) -> float: - """Return the voxel volume in mL from image spacing in mm.""" + """Return the voxel volume in milliliters. + + Parameters + ---------- + image : SimpleITK.Image + Image whose spacing defines the voxel dimensions. + + Returns + ------- + float + Voxel volume in milliliters. + """ spacing = image.GetSpacing()[:3] return float(spacing[0] * spacing[1] * spacing[2] / 1000.0) def _resolve_output_grid_reference( self, ref_time_id: int, output_grid: str ) -> Tuple[SimpleITK.Image, Optional[numpy.ndarray]]: - """Return the reference image and optional body mask for the requested output grid.""" + """Return the reference image and optional body mask for an output grid. + + Parameters + ---------- + ref_time_id : int + Time point used as the reference for image and mask lookup. + output_grid : str + Output grid selector. Supported values are ``"NM"`` and ``"CT"``. + + Returns + ------- + tuple + Reference image and an optional whole-body mask on that grid. + + Raises + ------ + ValueError + If ``output_grid`` is not supported. + """ if output_grid == "NM": return ( self.nm_data.images[ref_time_id], @@ -325,7 +397,13 @@ def _resolve_output_grid_reference( ) def run_MC(self) -> None: # TODO: finish the code!!!!! - """Run MC.""" + """Run the Monte Carlo dosimetry workflow. + + Raises + ------ + NotImplementedError + Always raised because the Monte Carlo workflow is not implemented yet. + """ raise NotImplementedError("MC is not implemmented yet.") n_cpu = self.config["#CPU"] n_primaries = self.config["#primaries"] @@ -395,9 +473,8 @@ def run_MC(self) -> None: # TODO: finish the code!!!!! def compute_dose(self) -> None: """Compute dose by performing the following steps. - Compute TIA at the region level. - Get parameters of fit from region and compute TIA at the voxel level. - Convolve TIA map with Dose kernel and (optional) scale with CT density. + Computes TIA at the region level, propagates the fit to the voxel level, + and then applies the configured voxel-S or Monte Carlo dose engine. """ self.compute_tia() self.compute_voxel_tia() From 118517f462c363039bb2de3900e81aa9e91fa80d Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 11 Aug 2026 12:45:14 -0700 Subject: [PATCH 22/27] Remove rstruct test segmentations from repo --- .../rtstructs/rtstruct_scan1.dcm | Bin 1100338 -> 0 bytes .../rtstructs/rtstruct_scan2.dcm | Bin 1096722 -> 0 bytes .../rtstructs/rtstruct_scan3.dcm | Bin 1080762 -> 0 bytes .../rtstructs/rtstruct_scan4.dcm | Bin 1102238 -> 0 bytes 4 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan1.dcm delete mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan2.dcm delete mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan3.dcm delete mode 100644 tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan4.dcm diff --git a/tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan1.dcm b/tests/data/voxel_dosimetry_validation/rtstructs/rtstruct_scan1.dcm deleted file mode 100644 index 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ze*T+(?*~8p;kWO<`Afg`tH1Vx-~GLB+vokwU;gJm__crWqwoLD?|l2=o4@#jAASG) z-;dw??eG8UuiyRVkAC=j-~NqX{o(I?{~P!J$?T4+{%8OH+KW-i7(E^T_I35*>FCAy px38-g Date: Tue, 11 Aug 2026 12:54:21 -0700 Subject: [PATCH 23/27] Update end-to-end dosimetry test with zenodo rtstructs --- .../data/voxel_dosimetry_validation/README.md | 10 ++- tests/test_voxel_dosimetry_validation.py | 88 +++++++++++++++++-- 2 files changed, 87 insertions(+), 11 deletions(-) diff --git a/tests/data/voxel_dosimetry_validation/README.md b/tests/data/voxel_dosimetry_validation/README.md index 3606e7c..5f74bf1 100644 --- a/tests/data/voxel_dosimetry_validation/README.md +++ b/tests/data/voxel_dosimetry_validation/README.md @@ -1,15 +1,19 @@ # Voxel Dosimetry Validation Assets -This directory stores optional assets for the slow voxel dosimetry validation test: +This directory stores lightweight reference assets for the slow voxel dosimetry +validation test: -- `rtstructs/`: precomputed RT-STRUCT DICOM files for the SNMMI dosimetry dataset - `expected_results.csv`: reference subset of `VoxelSDosimetry.results` - `expected_df_ad.csv`: reference subset of `VoxelSDosimetry.df_ad` - `voi_mappings_config.json` (optional): custom ROI mapping overrides for the validation run - `dosimetry_fit_defaults.json` (optional): custom ROI fit defaults for the validation run +The precomputed RT-STRUCT DICOM files are downloaded on demand from Zenodo +record `21893683`: https://zenodo.org/records/21893683. + The validation test is implemented in `tests/test_voxel_dosimetry_validation.py`. -It will skip automatically unless the RT-STRUCT directory and both CSV files are present. +It will skip automatically unless both CSV files are present or the remote +RT-STRUCT files cannot be fetched. Recommended contents of the CSV files: - Include only stable, comparable columns. diff --git a/tests/test_voxel_dosimetry_validation.py b/tests/test_voxel_dosimetry_validation.py index e421e2f..1fa601d 100644 --- a/tests/test_voxel_dosimetry_validation.py +++ b/tests/test_voxel_dosimetry_validation.py @@ -2,8 +2,11 @@ from __future__ import annotations +import json import re import shutil +import urllib.error +import urllib.request from pathlib import Path from typing import Final @@ -22,9 +25,20 @@ _VALIDATION_ASSETS: Final[Path] = ( Path(__file__).resolve().parent / "data" / "voxel_dosimetry_validation" ) -_RTSTRUCTS_DIR: Final[Path] = _VALIDATION_ASSETS / "rtstructs" _EXPECTED_RESULTS_CSV: Final[Path] = _VALIDATION_ASSETS / "expected_results.csv" _EXPECTED_DF_AD_CSV: Final[Path] = _VALIDATION_ASSETS / "expected_df_ad.csv" +_RTSTRUCT_ZENODO_RECORD_ID: Final[str] = "21893683" +_RTSTRUCT_ZENODO_RECORD_API_URL: Final[str] = ( + f"https://zenodo.org/api/records/{_RTSTRUCT_ZENODO_RECORD_ID}" +) +_RTSTRUCT_DOWNLOAD_TIMEOUT_S: Final[int] = 120 +_ZENODO_USER_AGENT: Final[str] = "PyTheranostics test suite" +_EXPECTED_RTSTRUCT_FILENAMES: Final[tuple[str, ...]] = ( + "rtstruct_scan1.dcm", + "rtstruct_scan2.dcm", + "rtstruct_scan3.dcm", + "rtstruct_scan4.dcm", +) _OPTIONAL_CONFIG_FILES: Final[tuple[str, ...]] = ( "voi_mappings_config.json", "dosimetry_fit_defaults.json", @@ -40,15 +54,15 @@ } -def _skip_if_validation_assets_missing() -> None: +def _skip_if_validation_reference_assets_missing() -> None: missing = [ path.name - for path in (_RTSTRUCTS_DIR, _EXPECTED_RESULTS_CSV, _EXPECTED_DF_AD_CSV) + for path in (_EXPECTED_RESULTS_CSV, _EXPECTED_DF_AD_CSV) if not path.exists() ] if missing: pytest.skip( - "Voxel dosimetry validation assets are not available. Missing: " + "Voxel dosimetry validation reference assets are not available. Missing: " + ", ".join(missing) + f". Populate {_VALIDATION_ASSETS} to enable this test." ) @@ -61,9 +75,60 @@ def _copy_optional_validation_configs(project_base: Path) -> None: shutil.copy2(src, project_base / config_name) -def _copy_rtstruct_assets(project_base: Path) -> None: +def _download_url_to_file(url: str, output_path: Path) -> None: + request = urllib.request.Request(url, headers={"User-Agent": _ZENODO_USER_AGENT}) + with urllib.request.urlopen( + request, timeout=_RTSTRUCT_DOWNLOAD_TIMEOUT_S + ) as response, output_path.open("wb") as output_file: + shutil.copyfileobj(response, output_file) + + +def _download_rtstruct_assets(project_base: Path) -> None: target_dir = project_base / "rtstructs" - shutil.copytree(_RTSTRUCTS_DIR, target_dir, dirs_exist_ok=True) + target_dir.mkdir(parents=True, exist_ok=True) + + request = urllib.request.Request( + _RTSTRUCT_ZENODO_RECORD_API_URL, + headers={"User-Agent": _ZENODO_USER_AGENT}, + ) + with urllib.request.urlopen( + request, timeout=_RTSTRUCT_DOWNLOAD_TIMEOUT_S + ) as response: + record = json.load(response) + + file_records = record.get("files", []) + if not file_records: + raise FileNotFoundError( + f"Zenodo record {_RTSTRUCT_ZENODO_RECORD_ID} does not list any files." + ) + + files_by_name = { + str(file_record.get("key", "")): file_record for file_record in file_records + } + missing = [ + filename + for filename in _EXPECTED_RTSTRUCT_FILENAMES + if filename not in files_by_name + ] + if missing: + discovered = sorted(filename for filename in files_by_name if filename) + raise FileNotFoundError( + "Zenodo RTSTRUCT assets are incomplete. Missing: " + + ", ".join(missing) + + ". Discovered: " + + (", ".join(discovered) if discovered else "none") + ) + + for filename in _EXPECTED_RTSTRUCT_FILENAMES: + links = files_by_name[filename].get("links", {}) + download_url = ( + links.get("content") or links.get("download") or links.get("self") + ) + if not download_url: + raise FileNotFoundError( + f"Zenodo file '{filename}' does not include a download link." + ) + _download_url_to_file(download_url, target_dir / filename) def _load_reference_frame(csv_path: Path) -> pd.DataFrame: @@ -173,7 +238,7 @@ def _prepare_frame_for_comparison( @pytest.mark.integration @pytest.mark.slow def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: - _skip_if_validation_assets_missing() + _skip_if_validation_reference_assets_missing() project_base = tmp_path / "snmmi_dosimetry_validation_project" init_project(project_base) @@ -184,7 +249,14 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: except RuntimeError as exc: pytest.skip(f"SNMMI dosimetry dataset could not be fetched: {exc}") - _copy_rtstruct_assets(project_base) + try: + _download_rtstruct_assets(project_base) + except ( + FileNotFoundError, + OSError, + urllib.error.URLError, + ) as exc: + pytest.skip(f"RTSTRUCT validation assets could not be fetched: {exc}") study_info, ct_paths, spect_paths, rtstruct_files = ( auto_setup_dosimetry_study_inventory( From bb8e6eeb1c87d09fb7df29333cc854e9a6f6f383 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 11 Aug 2026 13:12:46 -0700 Subject: [PATCH 24/27] Update test to use explicitly downloaded rtstrucs. --- tests/test_voxel_dosimetry_validation.py | 45 ++++++++++++++++++++++-- 1 file changed, 42 insertions(+), 3 deletions(-) diff --git a/tests/test_voxel_dosimetry_validation.py b/tests/test_voxel_dosimetry_validation.py index 1fa601d..57a8818 100644 --- a/tests/test_voxel_dosimetry_validation.py +++ b/tests/test_voxel_dosimetry_validation.py @@ -12,6 +12,7 @@ import numpy as np import pandas as pd +import pydicom import pytest from pandas.testing import assert_frame_equal @@ -33,6 +34,7 @@ ) _RTSTRUCT_DOWNLOAD_TIMEOUT_S: Final[int] = 120 _ZENODO_USER_AGENT: Final[str] = "PyTheranostics test suite" +_RTSTRUCT_SOP_CLASS_UID: Final[str] = "1.2.840.10008.5.1.4.1.1.481.3" _EXPECTED_RTSTRUCT_FILENAMES: Final[tuple[str, ...]] = ( "rtstruct_scan1.dcm", "rtstruct_scan2.dcm", @@ -83,7 +85,32 @@ def _download_url_to_file(url: str, output_path: Path) -> None: shutil.copyfileobj(response, output_file) -def _download_rtstruct_assets(project_base: Path) -> None: +def _validate_rtstruct_file(rtstruct_path: Path) -> None: + try: + ds = pydicom.dcmread(rtstruct_path, stop_before_pixels=True) + except Exception as exc: + raise ValueError(f"{rtstruct_path.name} is not a readable DICOM file.") from exc + + missing_sequences = [ + sequence_name + for sequence_name in ( + "ROIContourSequence", + "StructureSetROISequence", + "RTROIObservationsSequence", + ) + if not hasattr(ds, sequence_name) + ] + sop_class_uid = str(getattr(ds, "SOPClassUID", "")) + if sop_class_uid != _RTSTRUCT_SOP_CLASS_UID or missing_sequences: + raise ValueError( + f"{rtstruct_path.name} is not a valid RT Structure Set DICOM. " + f"Modality={getattr(ds, 'Modality', None)!r}, " + f"SOPClassUID={sop_class_uid!r}, " + f"missing sequences={missing_sequences or 'none'}." + ) + + +def _download_rtstruct_assets(project_base: Path) -> list[Path]: target_dir = project_base / "rtstructs" target_dir.mkdir(parents=True, exist_ok=True) @@ -128,7 +155,11 @@ def _download_rtstruct_assets(project_base: Path) -> None: raise FileNotFoundError( f"Zenodo file '{filename}' does not include a download link." ) - _download_url_to_file(download_url, target_dir / filename) + rtstruct_path = target_dir / filename + _download_url_to_file(download_url, rtstruct_path) + _validate_rtstruct_file(rtstruct_path) + + return [target_dir / filename for filename in _EXPECTED_RTSTRUCT_FILENAMES] def _load_reference_frame(csv_path: Path) -> pd.DataFrame: @@ -250,11 +281,12 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: pytest.skip(f"SNMMI dosimetry dataset could not be fetched: {exc}") try: - _download_rtstruct_assets(project_base) + downloaded_rtstruct_files = _download_rtstruct_assets(project_base) except ( FileNotFoundError, OSError, urllib.error.URLError, + ValueError, ) as exc: pytest.skip(f"RTSTRUCT validation assets could not be fetched: {exc}") @@ -273,6 +305,13 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: assert ( rtstruct_files ), "No RTSTRUCT files were discovered by the validation pipeline." + assert len(study_info["time_points"]) == len(downloaded_rtstruct_files) + + study_info["rtstruct_files"] = [str(path) for path in downloaded_rtstruct_files] + for time_point, rtstruct_file in zip( + study_info["time_points"], downloaded_rtstruct_files + ): + time_point["rtstruct_file"] = str(rtstruct_file) long_ct, long_spect, _, _ = create_studies_with_masks( patient_id=study_info["patient_id"], From 24b8e3cca1918598ed32dc8e212baa521f9ffc46 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 11 Aug 2026 13:19:36 -0700 Subject: [PATCH 25/27] Show verbose on skipped tests on CI --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 69ff892..92b7914 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -28,4 +28,4 @@ jobs: run: pre-commit run --all-files - name: Run pytest - run: pytest + run: pytest -rs From 32662216ea5dc397c7e32e5a60ce72075342387e Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 11 Aug 2026 13:43:09 -0700 Subject: [PATCH 26/27] Add guidance before PR --- CONTRIBUTING.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 964a736..1fec898 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -99,6 +99,9 @@ Once your environment is set up, you can use these commands: # Run all tests pytest +# Run the external voxel dosimetry validation test before opening a PR +pytest tests/test_voxel_dosimetry_validation.py -rs + # Run only smoke tests (fast) pytest -m smoke @@ -121,6 +124,13 @@ pre-commit run --all-files pytest -m smoke && black --check . && isort --check-only . && flake8 && mypy pytheranostics ``` +The voxel dosimetry validation test downloads the SNMMI Deep Blue dataset and +RT-STRUCT files from Zenodo. GitHub Actions may skip this test if Deep Blue +rejects CI-hosted downloads, so contributors should run it locally before +opening a pull request. Outside CI, SNMMI data-fetch failures are treated as +test failures rather than skips. Zenodo RT-STRUCT download or validation +failures are always test failures when the validation test runs. + ### Type Checking with Mypy Mypy is configured in `pyproject.toml` to be development-friendly (ignores missing imports by default). For focused development: From 023d02a3dffcedc20daa9954486fa05378dd5b12 Mon Sep 17 00:00:00 2001 From: "Pedro L. Esquinas" Date: Tue, 11 Aug 2026 13:43:43 -0700 Subject: [PATCH 27/27] Update end-to-end test to ensure only skips on CI if data can't be fetched. --- .../data/voxel_dosimetry_validation/README.md | 12 ++++++++-- tests/test_voxel_dosimetry_validation.py | 22 +++++++++++++++++-- 2 files changed, 30 insertions(+), 4 deletions(-) diff --git a/tests/data/voxel_dosimetry_validation/README.md b/tests/data/voxel_dosimetry_validation/README.md index 5f74bf1..55770a7 100644 --- a/tests/data/voxel_dosimetry_validation/README.md +++ b/tests/data/voxel_dosimetry_validation/README.md @@ -12,8 +12,16 @@ The precomputed RT-STRUCT DICOM files are downloaded on demand from Zenodo record `21893683`: https://zenodo.org/records/21893683. The validation test is implemented in `tests/test_voxel_dosimetry_validation.py`. -It will skip automatically unless both CSV files are present or the remote -RT-STRUCT files cannot be fetched. +GitHub Actions may skip this test when the external SNMMI Deep Blue dataset +rejects CI-hosted downloads. The Zenodo RT-STRUCT files are still expected to +download and validate when the test runs. Before opening a pull request, +developers should run it locally: + +```bash +pytest tests/test_voxel_dosimetry_validation.py -rs +``` + +Outside CI, SNMMI data-fetch failures fail the test instead of skipping it. Recommended contents of the CSV files: - Include only stable, comparable columns. diff --git a/tests/test_voxel_dosimetry_validation.py b/tests/test_voxel_dosimetry_validation.py index 57a8818..a630c13 100644 --- a/tests/test_voxel_dosimetry_validation.py +++ b/tests/test_voxel_dosimetry_validation.py @@ -3,6 +3,7 @@ from __future__ import annotations import json +import os import re import shutil import urllib.error @@ -56,6 +57,19 @@ } +def _skip_snmmi_fetch_failure_in_ci_or_fail_locally(reason: str) -> None: + message = ( + f"{reason}\n\n" + "GitHub Actions is allowed to skip this external validation test because " + "the SNMMI Deep Blue dataset can reject CI-hosted downloads. Before " + "opening a PR, run this test locally with:\n" + " pytest tests/test_voxel_dosimetry_validation.py -rs" + ) + if os.environ.get("CI", "").lower() == "true": + pytest.skip(message) + pytest.fail(message) + + def _skip_if_validation_reference_assets_missing() -> None: missing = [ path.name @@ -278,7 +292,9 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: try: fetch_snmmi_dosimetry_challenge(data_home=str(project_base)) except RuntimeError as exc: - pytest.skip(f"SNMMI dosimetry dataset could not be fetched: {exc}") + _skip_snmmi_fetch_failure_in_ci_or_fail_locally( + f"SNMMI dosimetry dataset could not be fetched: {exc}" + ) try: downloaded_rtstruct_files = _download_rtstruct_assets(project_base) @@ -288,7 +304,9 @@ def test_voxel_dosimetry_pipeline_matches_reference(tmp_path: Path) -> None: urllib.error.URLError, ValueError, ) as exc: - pytest.skip(f"RTSTRUCT validation assets could not be fetched: {exc}") + pytest.fail( + "RTSTRUCT validation assets could not be fetched or validated: " f"{exc}" + ) study_info, ct_paths, spect_paths, rtstruct_files = ( auto_setup_dosimetry_study_inventory(