diff --git a/mu_map/data/prepare.py b/mu_map/data/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7358029f57c26d8fd2b810ae85b43af008af453
--- /dev/null
+++ b/mu_map/data/prepare.py
@@ -0,0 +1,414 @@
+import argparse
+from datetime import datetime, timedelta
+from enum import Enum
+import os
+from typing import List
+
+import numpy as np
+import pandas as pd
+import pydicom
+
+
+class MyocardialProtocol(Enum):
+ Stress = 1
+ Rest = 2
+
+
+headers = argparse.Namespace()
+headers.id = "id"
+headers.patient_id = "patient_id"
+headers.age = "age"
+headers.weight = "weight"
+headers.size = "size"
+headers.protocol = "protocol"
+headers.datetime_acquisition = "datetime_acquisition"
+headers.datetime_reconstruction = "datetime_reconstruction"
+headers.pixel_spacing_x = "pixel_spacing_x"
+headers.pixel_spacing_y = "pixel_spacing_y"
+headers.pixel_spacing_z = "pixel_spacing_z"
+headers.shape_x = "shape_x"
+headers.shape_y = "shape_y"
+headers.shape_z = "shape_z"
+headers.radiopharmaceutical = "radiopharmaceutical"
+headers.radionuclide_dose = "radionuclide_dose"
+headers.radionuclide_code = "radionuclide_code"
+headers.radionuclide_meaning = "radionuclide_meaning"
+headers.energy_window_peak_lower = "energy_window_peak_lower"
+headers.energy_window_peak_upper = "energy_window_peak_upper"
+headers.energy_window_scatter_lower = "energy_window_scatter_lower"
+headers.energy_window_scatter_upper = "energy_window_scatter_upper"
+headers.detector_count = "detector_count"
+headers.collimator_type = "collimator_type"
+headers.rotation_start = "rotation_start"
+headers.rotation_step = "rotation_step"
+headers.rotation_scan_arc = "rotation_scan_arc"
+headers.file_recon_ac = "file_recon_ac"
+headers.file_recon_no_ac = "file_recon_no_ac"
+headers.file_mu_map = "file_mu_map"
+
+
+
+def parse_series_time(dicom_image: pydicom.dataset.FileDataset) -> datetime:
+ """
+ Parse the date and time of a DICOM series object into a datetime object.
+
+ :param dicom_image: the dicom file to parse the series date and time from
+ :return: an according python datetime object.
+ """
+ _date = dicom_image.SeriesDate
+ _time = dicom_image.SeriesTime
+ return datetime(
+ year=int(_date[0:4]),
+ month=int(_date[4:6]),
+ day=int(_date[6:8]),
+ hour=int(_time[0:2]),
+ minute=int(_time[2:4]),
+ second=int(_time[4:6]),
+ microsecond=int(_time.split(".")[1]),
+ )
+
+
+def parse_age(patient_age: str) -> int:
+ """
+ Parse and age string as defined in the DICOM standard into an integer representing the age in years.
+
+ :param patient_age: age string as defined in the DICOM standard
+ :return: the age in years as a number
+ """
+ assert (
+ type(patient_age) == str
+ ), f"patient age needs to be a string and not {type(patient_age)}"
+ assert (
+ len(patient_age) == 4
+ ), f"patient age [{patient_age}] has to be four characters long"
+ _num, _format = patient_age[:3], patient_age[3]
+ assert (
+ _format == "Y"
+ ), f"currently, only patient ages in years [Y] is supported, not [{_format}]"
+ return int(_num)
+
+
+def get_projection(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol) -> pydicom.dataset.FileDataset:
+ """
+ Extract the SPECT projection from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.
+
+ :param dicom_images: list of DICOM images of a study
+ :param protocol: the protocol for which the projection images should be extracted
+ :return: the extracted DICOM image
+ """
+ dicom_images = filter(lambda image: "TOMO" in image.ImageType, dicom_images)
+ dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)
+ dicom_images = list(dicom_images)
+
+ if len(dicom_images) != 1:
+ raise ValueError(f"No or multiple projections {len(dicom_images)} for protocol {protocol.name} available")
+
+ return dicom_images[0]
+
+
+def get_reconstruction(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol, corrected:bool=True) -> pydicom.dataset.FileDataset:
+ """
+ Extract a SPECT reconstruction from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.
+ The corrected flag can be used to either extract an attenuation corrected or a non-attenuation corrected image.
+ If there are multiple images, they are sorted by acquisition date and the newest is returned.
+
+ :param dicom_images: list of DICOM images of a study
+ :param protocol: the protocol for which the projection images should be extracted
+ :param corrected: extract an attenuation or non-attenuation corrected image
+ :return: the extracted DICOM image
+ """
+ dicom_images = filter(lambda image: "RECON TOMO" in image.ImageType, dicom_images)
+ dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)
+
+ if corrected:
+ dicom_images = filter(
+ lambda image: "AC" in image.SeriesDescription
+ and "NoAC" not in image.SeriesDescription,
+ dicom_images,
+ )
+ dicom_images = list(dicom_images)
+ else:
+ dicom_images = filter(
+ lambda image: "NoAC" in image.SeriesDescription, dicom_images
+ )
+
+ # for SPECT reconstructions created in clinical studies this value exists and is set to 'APEX_TO_BASE'
+ # for the reconstructions with attenuation maps it does not exist
+ dicom_images = filter(
+ lambda image: not hasattr(image, "SliceProgressionDirection"), dicom_images
+ )
+
+ dicom_images = list(dicom_images)
+ dicom_images.sort(key=lambda image: parse_series_time(image), reverse=True)
+
+ if len(dicom_images) == 0:
+ _str = "AC" if corrected else "NoAC"
+ raise ValueError(f"{_str} Reconstruction for protocol {protocol.name} is not available")
+
+ return dicom_images[0]
+
+
+def get_attenuation_map(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol) -> pydicom.dataset.FileDataset:
+ """
+ Extract an attenuation map from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.
+ If there are multiple attenuation maps, they are sorted by acquisition date and the newest is returned.
+
+ :param dicom_images: list of DICOM images of a study
+ :param protocol: the protocol for which the projection images should be extracted
+ :return: the extracted DICOM image
+ """
+ dicom_images = filter(lambda image: "RECON TOMO" in image.ImageType, dicom_images)
+ dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)
+ dicom_images = filter(lambda image: "µ-map" in image.SeriesDescription, dicom_images)
+ dicom_images = list(dicom_images)
+ dicom_images.sort(key=lambda image: parse_series_time(image), reverse=True)
+
+ if len(dicom_images) == 0:
+ raise ValueError(f"Attenuation map for protocol {protocol.name} is not available")
+
+ return dicom_images[0]
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="Prepare a dataset from DICOM directories",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "dicom_dirs",
+ type=str,
+ nargs="+",
+ help="paths to DICOMDIR files or directories containing one of them",
+ )
+ parser.add_argument("--dataset_dir", type=str, required=True, help="")
+ parser.add_argument("--images_dir", type=str, default="images", help="")
+ parser.add_argument("--csv", type=str, default="data.csv", help="")
+ parser.add_argument("--prefix_projection", type=str, default="projection", help="")
+ parser.add_argument("--prefix_mu_map", type=str, default="mu_map", help="")
+ parser.add_argument("--prefix_recon_ac", type=str, default="recon_ac", help="")
+ parser.add_argument("--prefix_recon_no_ac", type=str, default="recon_no_ac", help="")
+ args = parser.parse_args()
+
+ args.dicom_dirs = [
+ (os.path.dirname(_file) if os.path.isfile(_file) else _file)
+ for _file in args.dicom_dirs
+ ]
+ args.images_dir = os.path.join(args.dataset_dir, args.images_dir)
+ args.csv = os.path.join(args.dataset_dir, args.csv)
+
+ patients = []
+ dicom_dir_by_patient = {}
+ for dicom_dir in args.dicom_dirs:
+ dataset = pydicom.dcmread(os.path.join(dicom_dir, "DICOMDIR"))
+ for patient in dataset.patient_records:
+ assert (
+ patient.PatientID not in dicom_dir_by_patient
+ ), f"Patient {patient.PatientID} is contained twice in the given DICOM directories ({dicom_dir} and {dicom_dir_by_patient[patient.PatientID]})"
+ dicom_dir_by_patient[patient.PatientID] = dicom_dir
+ patients.append(patient)
+
+ if not os.path.exists(args.dataset_dir):
+ os.mkdir(args.dataset_dir)
+
+ if not os.path.exists(args.images_dir):
+ os.mkdir(args.images_dir)
+
+ _id = 1
+ if os.path.exists(args.csv):
+ data = pd.read_csv(args.csv)
+ _id = int(data[headers.id].max())
+ else:
+ data = pd.DataFrame(dict([(key, []) for key in vars(headers).keys()]))
+
+
+ for i, patient in enumerate(patients, start=1):
+ print(f"Process patient {str(i):>3}/{len(patients)}:")
+
+ # get all myocardial scintigraphy studies
+ studies = list(
+ filter(
+ lambda child: child.DirectoryRecordType == "STUDY"
+ and child.StudyDescription == "Myokardszintigraphie",
+ patient.children,
+ )
+ )
+
+ # extract all dicom images
+ dicom_images = []
+ for study in studies:
+ series = list(
+ filter(lambda child: child.DirectoryRecordType == "SERIES", study.children)
+ )
+ for _series in series:
+ images = list(
+ filter(
+ lambda child: child.DirectoryRecordType == "IMAGE", _series.children
+ )
+ )
+
+ # all SPECT data is stored as a single 3D array which means that it is a series with a single image
+ # this is not the case for CTs, which are skipped here
+ if len(images) != 1:
+ continue
+
+ images = list(
+ map(
+ lambda image: pydicom.dcmread(
+ os.path.join(dicom_dir_by_patient[patient.PatientID], *image.ReferencedFileID),
+ stop_before_pixels=True,
+ ),
+ images,
+ )
+ )
+
+ if len(images) == 0:
+ continue
+
+ dicom_images.append(images[0])
+
+
+ for protocol in MyocardialProtocol:
+ if len(data[(data[headers.patient_id] == patient.PatientID) & (data[headers.protocol] == protocol.name)]) > 0:
+ print(f"Skip {patient.PatientID}:{protocol.name} since it is already contained in the dataset")
+ continue
+
+ try:
+ projection_image = get_projection(dicom_images, protocol=protocol)
+ recon_ac = get_reconstruction(dicom_images, protocol=protocol, corrected=True)
+ recon_noac = get_reconstruction(dicom_images, protocol=protocol, corrected=False)
+ attenuation_map = get_attenuation_map(dicom_images, protocol=protocol)
+ except ValueError as e:
+ print(f"Skip {patient.PatientID}:{protocol.name} because {e}")
+ continue
+
+ recon_images = [recon_ac, recon_noac, attenuation_map]
+
+ # extract date times and assert that they are equal for all reconstruction images
+ datetimes = list(map(parse_series_time, recon_images))
+ _datetimes = sorted(datetimes, reverse=True)
+ _datetimes_delta = list(map(lambda dt: _datetimes[0] - dt, _datetimes))
+ _equal = all(map(lambda dt: dt < timedelta(seconds=300), _datetimes_delta))
+ assert (
+ _equal
+ ), f"Not all dates and times of the reconstructions are equal: {datetimes}"
+
+ # extract pixel spacings and assert that they are equal for all reconstruction images
+ pixel_spacings = map(
+ lambda image: [*image.PixelSpacing, image.SliceThickness], recon_images
+ )
+ pixel_spacings = map(
+ lambda pixel_spacing: list(map(float, pixel_spacing)), pixel_spacings
+ )
+ pixel_spacings = map(
+ lambda pixel_spacing: np.array(pixel_spacing), pixel_spacings
+ )
+ pixel_spacings = list(pixel_spacings)
+ _equal = all(
+ map(
+ lambda pixel_spacing: (pixel_spacing == pixel_spacings[0]).all(),
+ pixel_spacings,
+ )
+ )
+ assert (
+ _equal
+ ), f"Not all pixel spacings of the reconstructions are equal: {pixel_spacings}"
+ pixel_spacing = pixel_spacings[0]
+
+ # extract shapes and assert that they are equal for all reconstruction images
+ shapes = map(
+ lambda image: [image.Rows, image.Columns, image.NumberOfSlices],
+ recon_images,
+ )
+ shapes = map(lambda shape: list(map(int, shape)), shapes)
+ shapes = map(lambda shape: np.array(shape), shapes)
+ shapes = list(shapes)
+ _equal = all(map(lambda shape: (shape == shapes[0]).all(), shapes))
+ # assert _equal, f"Not all shapes of the reconstructions are equal: {shapes}"
+ # print(shapes)
+ shape = shapes[0]
+
+ # exctract and sort energy windows
+ energy_windows = projection_image.EnergyWindowInformationSequence
+ energy_windows = map(
+ lambda ew: ew.EnergyWindowRangeSequence[0], energy_windows
+ )
+ energy_windows = map(
+ lambda ew: (
+ float(ew.EnergyWindowLowerLimit),
+ float(ew.EnergyWindowUpperLimit),
+ ),
+ energy_windows,
+ )
+ energy_windows = list(energy_windows)
+ energy_windows.sort(key=lambda ew: ew[0], reverse=True)
+
+ # re-read images with pixel-level data and save accordingly
+ projection_image = pydicom.dcmread(projection_image.filename)
+ recon_ac = pydicom.dcmread(recon_ac.filename)
+ recon_noac = pydicom.dcmread(recon_noac.filename)
+ attenuation_map = pydicom.dcmread(attenuation_map.filename)
+
+ pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_projection}.dcm"), projection_image)
+ pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_recon_ac}.dcm"), recon_ac)
+ pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_recon_no_ac}.dcm"), recon_noac)
+ pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_mu_map}.dcm"), attenuation_map)
+
+ row = {
+ headers.id: _id,
+ headers.patient_id: projection_image.PatientID,
+ headers.age: parse_age(projection_image.PatientAge),
+ headers.weight: float(projection_image.PatientWeight),
+ headers.size: float(projection_image.PatientSize),
+ headers.protocol: protocol.name,
+ headers.datetime_acquisition: parse_series_time(projection_image),
+ headers.datetime_reconstruction: datetimes[0],
+ headers.pixel_spacing_x: pixel_spacing[0],
+ headers.pixel_spacing_y: pixel_spacing[1],
+ headers.pixel_spacing_z: pixel_spacing[2],
+ headers.shape_x: shape[0],
+ headers.shape_y: shape[1],
+ headers.shape_z: shape[2],
+ headers.radiopharmaceutical: projection_image.RadiopharmaceuticalInformationSequence[
+ 0
+ ].Radiopharmaceutical,
+ headers.radionuclide_dose: projection_image.RadiopharmaceuticalInformationSequence[
+ 0
+ ].RadionuclideTotalDose,
+ headers.radionuclide_code: projection_image.RadiopharmaceuticalInformationSequence[
+ 0
+ ]
+ .RadionuclideCodeSequence[0]
+ .CodeValue,
+ headers.radionuclide_meaning: projection_image.RadiopharmaceuticalInformationSequence[
+ 0
+ ]
+ .RadionuclideCodeSequence[0]
+ .CodeMeaning,
+ headers.energy_window_peak_lower: energy_windows[0][0],
+ headers.energy_window_peak_upper: energy_windows[0][1],
+ headers.energy_window_scatter_lower: energy_windows[1][0],
+ headers.energy_window_scatter_upper: energy_windows[1][1],
+ headers.detector_count: len(projection_image.DetectorInformationSequence),
+ headers.collimator_type: projection_image.DetectorInformationSequence[
+ 0
+ ].CollimatorType,
+ headers.rotation_start: float(
+ projection_image.RotationInformationSequence[0].StartAngle
+ ),
+ headers.rotation_step: float(
+ projection_image.RotationInformationSequence[0].AngularStep
+ ),
+ headers.rotation_scan_arc: float(
+ projection_image.RotationInformationSequence[0].ScanArc
+ ),
+ headers.file_recon_ac: "filename_recon_ac.dcm",
+ headers.file_recon_no_ac: "filename_recon_no_ac.dcm",
+ headers.file_mu_map: "filanem_mu_map.dcm",
+ }
+ _id += 1
+
+ row = pd.DataFrame(row, index=[0])
+ data = pd.concat((data, row), ignore_index=True)
+
+data.to_csv(args.csv, index=False)
+