def __init__(self, progress_callback, interrupt_flag, patient_files,
input_path):
"""
Class initialiser function.
:param progress_callback: A signal that receives the current
progress of the loading.
:param interrupt_flag: A threading.Event() object that tells the
function to stop loading.
:param patient_files: List of patient files.
:param output_path: Path of the input CSV file.
"""
# Call the parent class
super(BatchProcessCSV2ClinicalDataSR,
self).__init__(progress_callback, interrupt_flag, patient_files)
# Set class variables
self.patient_dict_container = PatientDictContainer()
self.required_classes = ['ct', 'rtdose']
self.required_classes_2 = ['pet', 'rtdose']
# Only need one of either ct or pet (and rtdose)
self.ready = False
ready = self.load_images(patient_files, self.required_classes)
if ready:
self.ready = True
else:
self.ready = \
self.load_images(patient_files, self.required_classes_2)
self.input_path = input_path
def onSaveClicked(self):
""" Save the new ROI """
# Get the name of the new ROI
new_roi_name = self.new_roi_name_line_edit.text()
# If the new ROI hasn't been drawn, draw the new ROI. Then if the new
# ROI is drawn successfully, proceed to save the new ROI.
if self.new_ROI_contours is None:
if not self.onDrawButtonClicked():
return
# Get a dict to convert SOPInstanceUID to slice id
slice_ids_dict = get_dict_slice_to_uid(PatientDictContainer())
# Transform new_ROI_contours to a list of roi information
rois_to_save = {}
for uid, contour_sequence in self.new_ROI_contours.items():
slider_id = slice_ids_dict[uid]
location = self.patient_dict_container.filepaths[slider_id]
ds = pydicom.dcmread(location)
slice_info = {'coords': contour_sequence, 'ds': ds}
rois_to_save[slider_id] = slice_info
roi_list = ROI.convert_hull_list_to_contours_data(
rois_to_save, self.patient_dict_container)
connectSaveROIProgress(self, roi_list, self.dataset_rtss, new_roi_name,
self.roi_saved)
def cleanup(self):
patient_dict_container = PatientDictContainer()
patient_dict_container.clear()
# Close 3d vtk widget
self.three_dimension_view.close()
self.cleanup_image_fusion()
self.cleanup_pt_ct_viewer()
def test_save_radiomics_data():
"""
Test for saving pyradiomics data to a DICOM SR file.
"""
# Get test data files
# Load test DICOM files
desired_path = Path.cwd().joinpath('test', 'testdata')
# list of DICOM test files
selected_files = find_DICOM_files(desired_path)
# file path of DICOM files
file_path = os.path.dirname(os.path.commonprefix(selected_files))
read_data_dict, file_names_dict = \
ImageLoading.get_datasets(selected_files)
# Create patient dict container object
patient_dict_container = PatientDictContainer()
patient_dict_container.clear()
patient_dict_container.set_initial_values(file_path, read_data_dict,
file_names_dict)
file_path = patient_dict_container.path
file_path = Path(file_path).joinpath("PyRadiomics-SR.dcm")
ds = patient_dict_container.dataset[0]
dicom_sr = DICOMStructuredReport.generate_dicom_sr(file_path, ds, "text",
"PYRADIOMICS")
dicom_sr.save_as(file_path)
# Assert that the new SR exists
assert os.path.isfile(file_path)
# Delete the created DICOM SR
os.remove(file_path)
def test_create_roi():
rt_ss = dataset.Dataset()
rt_ss.StructureSetROISequence = []
rt_ss.StructureSetROISequence.append(dataset.Dataset())
rt_ss.StructureSetROISequence[0].ReferencedFrameOfReferenceUID = "1.2.3"
rt_ss.StructureSetROISequence[0].ROINumber = "1"
rt_ss.ROIContourSequence = []
rt_ss.RTROIObservationsSequence = []
roi_name = "NewTestROI"
roi_coordinates = [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0] # a closed right triangle
image_ds = dataset.Dataset()
image_ds.SOPClassUID = "1.2.840.10008.5.1.4.1.1.2"
image_ds.SOPInstanceUID = "1.2.3.4.5.6.7.8.9"
patient_dict_container = PatientDictContainer()
# container has to be initialised with kwargs content for the get/set to not fail for lack of an
# "additional_parameters" dict.
patient_dict_container.set_initial_values(None,
None,
None,
blah="blah",
rois={})
if patient_dict_container.get("rois") is not None:
print("rois are present in patient dict container")
updated_rtss = create_roi(rt_ss, roi_name, roi_coordinates, image_ds)
first_contour = updated_rtss.ROIContourSequence[0].ContourSequence[0]
assert (first_contour.ContourImageSequence[0].ReferencedSOPClassUID ==
image_ds.SOPClassUID)
assert (first_contour.ContourGeometricType == "CLOSED_PLANAR")
assert (rt_ss.RTROIObservationsSequence[0].RTROIInterpretedType == "ORGAN")
def batch_iso2roi_handler(self, interrupt_flag, progress_callback,
patient):
"""
Handles creating, starting, and processing the results of batch
ISO2ROI.
:param interrupt_flag: A threading.Event() object that tells the
function to stop loading.
:param progress_callback: A signal that receives the current
progress of the loading.
:param patient: The patient to perform this process on.
"""
# Get current patient files
cur_patient_files = \
BatchProcessingController.get_patient_files(patient)
# Create and start process
process = BatchProcessISO2ROI(progress_callback, interrupt_flag,
cur_patient_files)
success = process.start()
# Add rtss to patient in case it is needed in future
# processes
if success:
if PatientDictContainer().get("rtss_modified"):
self.update_rtss(patient)
reason = "SUCCESS"
else:
reason = process.summary
# Append process summary
if patient not in self.batch_summary[0].keys():
self.batch_summary[0][patient] = {}
self.batch_summary[0][patient]["iso2roi"] = reason
progress_callback.emit(("Completed ISO2ROI", 100))
def populate_table(self):
"""
Populates the table with data from the DICOM-SR file, if it
exists.
"""
# Attempt to get clinical data dataset
patient_dict_container = PatientDictContainer()
try:
clinical_data = patient_dict_container.dataset['sr-cd']
# Get text from clinical data dataset
text = clinical_data.ContentSequence[0].TextValue
# Split text into dictionary
text_data = text.splitlines()
for row in text_data:
key, value = row.split(":")
self.data_dict[key] = value
except KeyError:
# See if data has been imported
if self.data_dict:
pass
else:
return
# Populate table with loaded values
for i, key in enumerate(self.data_dict):
attrib = QtWidgets.QTableWidgetItem(key)
value = QtWidgets.QTableWidgetItem(self.data_dict[key])
self.table_cd.insertRow(i)
self.table_cd.setItem(i, 0, attrib)
self.table_cd.setItem(i, 1, value)
self.table_populated = True
def __init__(self):
# Load test DICOM files
desired_path = Path.cwd().joinpath('test', 'testdata')
selected_files = find_DICOM_files(desired_path) # list of DICOM test files
file_path = os.path.dirname(os.path.commonprefix(selected_files)) # file path of DICOM files
read_data_dict, file_names_dict = ImageLoading.get_datasets(selected_files)
# Create patient dict container object
patient_dict_container = PatientDictContainer()
patient_dict_container.clear()
patient_dict_container.set_initial_values(file_path, read_data_dict, file_names_dict)
# Set additional attributes in patient dict container (otherwise program will crash and test will fail)
if "rtss" in file_names_dict:
dataset_rtss = dcmread(file_names_dict['rtss'])
self.rois = ImageLoading.get_roi_info(dataset_rtss)
dict_raw_contour_data, dict_numpoints = ImageLoading.get_raw_contour_data(dataset_rtss)
dict_pixluts = ImageLoading.get_pixluts(read_data_dict)
patient_dict_container.set("rois", self.rois)
patient_dict_container.set("raw_contour", dict_raw_contour_data)
patient_dict_container.set("num_points", dict_numpoints)
patient_dict_container.set("pixluts", dict_pixluts)
# Open the main window
self.main_window = MainWindow()
def __init__(self):
# Load test DICOM files and set path variable
path = Path.cwd().joinpath('test', 'testdata')
files = get_dicom_files(path) # list of DICOM test files
file_path = os.path.dirname(os.path.commonprefix(files))
read_data_dict, file_names_dict = ImageLoading.get_datasets(files)
# Create patient dict container object
patient_dict_container = PatientDictContainer()
patient_dict_container.clear()
patient_dict_container.set_initial_values(file_path, read_data_dict,
file_names_dict)
# Set additional attributes in patient dict container
# This prevents crashes
if "rtss" in file_names_dict:
dataset_rtss = dcmread(file_names_dict['rtss'])
self.rois = ImageLoading.get_roi_info(dataset_rtss)
patient_dict_container.set("rois", self.rois)
# Open the main window
self.main_window = MainWindow()
self.main_window.right_panel.setCurrentWidget(
self.main_window.dicom_tree)
self.dicom_tree = self.main_window.dicom_tree
def on_loaded_suv2roi(self):
"""
Called when progress bar has finished. Closes the progress
window and refreshes the main screen.
"""
if self.suv2roi.suv2roi_status:
patient_dict_container = PatientDictContainer()
self.structures_tab.fixed_container_structure_modified((
patient_dict_container.get('dataset_rtss'), {"draw": None}))
else:
# Alert user that SUV2ROI failed and for what reason
if self.suv2roi.failure_reason == "UNIT":
failure_reason = \
"PET units are not Bq/mL. OnkoDICOM can currently only\n" \
"perform SUV2ROI on PET images stored in these units."
elif self.suv2roi.failure_reason == "DECY":
failure_reason = \
"PET is not decay corrected. OnkoDICOM can currently " \
"only\nperform SUV2ROI on PET images that are decay " \
"corrected."
else:
failure_reason = "The SUV2ROI process has failed."
button_reply = \
QtWidgets.QMessageBox(
QtWidgets.QMessageBox.Icon.Warning,
"SUV2ROI Failed",
failure_reason,
QtWidgets.QMessageBox.StandardButton.Ok, self)
button_reply.button(
QtWidgets.QMessageBox.StandardButton.Ok).setStyleSheet(
self.stylesheet)
button_reply.exec_()
# Close progress window
self.suv2roi_progress_window.close()
def __init__(self):
QtWidgets.QWidget.__init__(self)
self.patient_dict_container = PatientDictContainer()
self.rx_dose_in_cgray = self.patient_dict_container.get(
"rx_dose_in_cgray")
self.color_dict = self.init_color_isod()
self.color_squares = self.init_color_squares()
self.checkboxes = self.init_checkboxes()
# Create and initialise ISO2ROI button and layout
self.iso2roi_button = QtWidgets.QPushButton()
self.iso2roi_button.setText("Convert Isodoses to ROIs")
self.iso2roi_button.clicked.connect(self.iso2roi_button_clicked)
self.iso2roi_layout = QtWidgets.QHBoxLayout()
self.iso2roi_layout.setContentsMargins(0, 0, 0, 0)
self.iso2roi_layout.addWidget(self.iso2roi_button)
self.isodose_tab_layout = QtWidgets.QVBoxLayout()
self.isodose_tab_layout.setAlignment(QtCore.Qt.AlignTop
| QtCore.Qt.AlignTop)
self.isodose_tab_layout.setSpacing(0)
self.init_layout()
self.iso2roi = ISO2ROI()
# Add button to tab
self.isodose_tab_layout.addStretch()
self.isodose_tab_layout.addLayout(self.iso2roi_layout)
self.setLayout(self.isodose_tab_layout)
self.progress_window = ProgressWindow(
self, QtCore.Qt.WindowTitleHint | QtCore.Qt.WindowCloseButtonHint)
self.progress_window.signal_loaded.connect(self.on_loaded_iso2roi)
def save_rtss(cls):
"""
Saves the RT Struct.
"""
patient_dict_container = PatientDictContainer()
rtss_directory = Path(patient_dict_container.get("file_rtss"))
patient_dict_container.get("dataset_rtss").save_as(rtss_directory)
def save_clinical_data(self):
"""
Saves clinical data to a DICOM-SR file. Overwrites any existing
clinical data SR files in the dataset.
"""
# Only save if there is at least one row in the table
if self.table_cd.rowCount() <= 0:
return
# Create string from clinical data dictionary
text = ""
for key in self.data_dict:
text += str(key) + ": " + str(self.data_dict[key]) + "\n"
# Create and save DICOM SR file
patient_dict_container = PatientDictContainer()
file_path = patient_dict_container.path
file_path = Path(file_path).joinpath("Clinical-Data-SR.dcm")
ds = patient_dict_container.dataset[0]
dicom_sr = DICOMStructuredReport.generate_dicom_sr(
file_path, ds, text, "CLINICAL-DATA")
dicom_sr.save_as(file_path)
# Update patient dict container
patient_dict_container.dataset['sr-cd'] = dicom_sr
patient_dict_container.filepaths['sr-cd'] = file_path
def __init__(self, *args, **kwargs):
super(CalculateDVHProgressWindow, self).__init__(*args, **kwargs)
layout = QtWidgets.QVBoxLayout()
text = QtWidgets.QLabel(
"Calculating DVHs... (This may take several minutes)")
layout.addWidget(text)
self.setWindowTitle("Please wait...")
self.setLayout(layout)
self.threadpool = QtCore.QThreadPool()
self.patient_dict_container = PatientDictContainer()
dataset_rtss = self.patient_dict_container.dataset["rtss"]
dataset_rtdose = self.patient_dict_container.dataset["rtdose"]
rois = self.patient_dict_container.get("rois")
dict_thickness = ImageLoading.get_thickness_dict(
dataset_rtss, self.patient_dict_container.dataset)
interrupt_flag = threading.Event()
fork_safe_platforms = ['Linux']
if platform.system() in fork_safe_platforms:
worker = Worker(ImageLoading.multi_calc_dvh, dataset_rtss,
dataset_rtdose, rois, dict_thickness)
else:
worker = Worker(ImageLoading.calc_dvhs, dataset_rtss,
dataset_rtdose, rois, dict_thickness,
interrupt_flag)
worker.signals.result.connect(self.dvh_calculated)
self.threadpool.start(worker)
def __init__(self, roi_color=None, iso_color=None):
QtWidgets.QWidget.__init__(self)
self.patient_dict_container = PatientDictContainer()
self.iso_color = iso_color
self.zoom = 1
self.current_slice_number = None
self.dicom_view_layout = QtWidgets.QHBoxLayout()
# Create components
self.slider = QtWidgets.QSlider(QtCore.Qt.Vertical)
self.init_slider()
self.view = QtWidgets.QGraphicsView()
self.init_view()
self.scene = QtWidgets.QGraphicsScene()
# Set layout
self.dicom_view_layout.addWidget(self.view)
self.dicom_view_layout.addWidget(self.slider)
self.setLayout(self.dicom_view_layout)
# Init metadata widgets
self.metadata_layout = QtWidgets.QVBoxLayout(self.view)
self.label_image_id = QtWidgets.QLabel()
self.label_image_pos = QtWidgets.QLabel()
self.label_wl = QtWidgets.QLabel()
self.label_image_size = QtWidgets.QLabel()
self.label_zoom = QtWidgets.QLabel()
self.label_patient_pos = QtWidgets.QLabel()
self.init_metadata()
self.update_view()
def __init__(self):
QtWidgets.QWidget.__init__(self)
self.patient_dict_container = PatientDictContainer()
self.dvh_calculated = self.patient_dict_container.has_attribute(
"raw_dvh")
self.rt_dose = self.patient_dict_container.dataset['rtdose']
self.raw_dvh = None
self.dvh_x_y = None
self.plot = None
self.selected_rois = self.patient_dict_container.get("selected_rois")
self.dvh_tab_layout = QtWidgets.QVBoxLayout()
try:
# Import the DVH from RT Dose
self.import_rtdose()
except (AttributeError, KeyError):
# Construct the layout based on whether or not the DVH has
# already been calculated.
# TODO: convert to logging
print("DVH data not in RT Dose.")
if self.dvh_calculated:
self.init_layout_dvh()
else:
self.init_layout_no_dvh()
self.setLayout(self.dvh_tab_layout)
def __init__(self):
# Load test DICOM files
desired_path = Path.cwd().joinpath('test', 'pet-testdata')
# list of DICOM test files
selected_files = find_dicom_files(desired_path)
# file path of DICOM files
file_path = os.path.dirname(os.path.commonprefix(selected_files))
read_data_dict, file_names_dict = \
ImageLoading.get_datasets(selected_files)
# Create patient dict container object
self.patient_dict_container = PatientDictContainer()
self.patient_dict_container.clear()
self.patient_dict_container.set_initial_values \
(file_path, read_data_dict, file_names_dict)
# Create variables to be initialised later
self.dicom_files = None
self.suv_data = []
# Create SUV2ROI object
self.suv2roi = SUV2ROI()
# Set patient weight. Not actual weight, just for testing
# purposes.
self.suv2roi.patient_weight = 70000
def update_ui(self):
# Instantiate a local new PatientDictContainer
patient_dict_container = PatientDictContainer()
patient = patient_dict_container.get("basic_info")
# Compare local patient with previous instance of ImageFusion
if self.patient_id != patient['id']:
self.update_patient()
def update_patient(self):
self.clear_checked_leaves()
self.patient_dict_container = PatientDictContainer()
self.patient = self.patient_dict_container.get("basic_info")
self.patient_id = self.patient['id']
dataset = self.patient_dict_container.dataset[0]
self.patient_current_image_series_uid = \
dataset.get("SeriesInstanceUID")
def update_ui(self, moving=False):
"""
Update the UI of Structure Tab when a new patient is opened
"""
self.patient_dict_container = PatientDictContainer()
self.rois = self.patient_dict_container.get("rois")
self.color_dict = self.init_color_roi(self.patient_dict_container)
self.patient_dict_container.set("roi_color_dict", self.color_dict)
if hasattr(self, "modified_indicator_widget"):
self.modified_indicator_widget.setParent(None)
self.update_content()
def get_fused_window(level, window):
"""
Apply windowing on the fixed and moving (linear-registered) images.
Args:
level(int): the level (midpoint) of windowing
window(any): the window (range) of windowing
Return:
color_axial (QtGui.QPixmap): pixmap of the registered image from axial
view
color_sagittal (QtGui.QPixmap): pixmap of the registered image from
sagittal view
color_coronal (QtGui.QPixmap): pixmap of the registered image from
coronal view
tfm (sitk.CompositeTransform): transformation object containing data
that is a product from linear_registration
"""
patient_dict_container = PatientDictContainer()
old_images = patient_dict_container.get("sitk_original")
fused_image = patient_dict_container.get("fused_images")
tfm = fused_image[1]
array = sitk.GetArrayFromImage(old_images).shape
axial_slice_count = array[0]
coronal_slice_count = array[1]
sagittal_slice_count = array[1]
sp_plane, _, sp_slice = old_images.GetSpacing()
asp = (1.0 * sp_slice) / sp_plane
color_axial = {}
color_sagittal = {}
color_coronal = {}
windowing = (int(level - CT_RESCALE_INTERCEPT), int(window))
for i in range(axial_slice_count):
color_axial[i] = \
get_fused_pixmap(old_images, fused_image[0], asp, i, "axial",
windowing)
for i in range(sagittal_slice_count):
color_sagittal[i] = \
get_fused_pixmap(old_images, fused_image[0], asp, i, "sagittal",
windowing)
for i in range(coronal_slice_count):
color_coronal[i] = \
get_fused_pixmap(old_images, fused_image[0], asp, i, "coronal",
windowing)
return color_axial, color_sagittal, color_coronal, tfm
def update_rtss(self, patient):
"""
Updates the patient dict container with the newly created RTSS (if a
process generates one), so it can be used by future processes.
:param patient: The patient with the newly-created RTSS.
"""
# Get new RTSS
rtss = PatientDictContainer().dataset['rtss']
# Create a series and image from the RTSS
rtss_series = Series(rtss.SeriesInstanceUID)
rtss_series.series_description = rtss.get("SeriesDescription")
rtss_image = Image(PatientDictContainer().filepaths['rtss'],
rtss.SOPInstanceUID, rtss.SOPClassUID,
rtss.Modality)
rtss_series.add_image(rtss_image)
# Add the new study to the patient
patient.studies[rtss.StudyInstanceUID].add_series(rtss_series)
# Update the patient dict container
PatientDictContainer().set("rtss_modified", False)
def read_images_for_fusion(level=0, window=0):
"""
Performs initial image fusion, this is by converting the old and
new images for transformations into SITK object. Images are co-registered
using SITK library. Images and SITK.CompositeTransformation objects are
added to the patient dataset.
Args:
level(int): midpoint of window
window(Any): range of values, should at least contain low bound and
high bound
"""
patient_dict_container = PatientDictContainer()
moving_dict_container = MovingDictContainer()
if level == 0 or window == 0:
level = patient_dict_container.get("level")
window = patient_dict_container.get("window")
amount = len(patient_dict_container.filepaths)
orig_fusion_list = []
for i in range(amount):
try:
orig_fusion_list.append(patient_dict_container.filepaths[i])
except KeyError:
continue
orig_image = sitk.ReadImage(orig_fusion_list)
patient_dict_container.set("sitk_original", orig_image)
amount = len(moving_dict_container.filepaths)
new_fusion_list = []
for i in range(amount):
try:
new_fusion_list.append(moving_dict_container.filepaths[i])
except KeyError:
continue
new_image = sitk.ReadImage(new_fusion_list)
moving_dict_container.set("sitk_moving", new_image)
create_fused_model(orig_image, new_image)
color_axial, color_sagittal, color_coronal, tfm = \
get_fused_window(level, window)
patient_dict_container.set("color_axial", color_axial)
patient_dict_container.set("color_sagittal", color_sagittal)
patient_dict_container.set("color_coronal", color_coronal)
moving_dict_container.set("tfm", tfm)
def __init__(self):
QtWidgets.QWidget.__init__(self)
self.patient_dict_container = PatientDictContainer()
self.rx_dose_in_cgray = self.patient_dict_container.get("rx_dose_in_cgray")
self.color_dict = self.init_color_isod()
self.color_squares = self.init_color_squares()
self.checkboxes = self.init_checkboxes()
self.isodose_tab_layout = QtWidgets.QVBoxLayout()
self.isodose_tab_layout.setAlignment(QtCore.Qt.AlignTop)
self.isodose_tab_layout.setSpacing(0)
self.init_layout()
self.setLayout(self.isodose_tab_layout)
def perform_suv2roi(self):
"""
Performs the SUV2ROI process.
"""
# Get patient weight - needs to run first as GUI cannot run in
# threads, like the ProgressBar
patient_dict_container = PatientDictContainer()
dataset = patient_dict_container.dataset[0]
self.suv2roi.get_patient_weight(dataset)
if self.suv2roi.patient_weight is None:
return
# Start the SUV2ROI process
self.suv2roi_progress_window.start(self.suv2roi.start_conversion)
def __init__(self, action_handler: ActionHandler):
QToolBar.__init__(self)
self.action_handler = action_handler
self.patient_dict_container = PatientDictContainer()
self.setCursor(QCursor(Qt.PointingHandCursor))
self.setMovable(False)
self.setFloatable(False)
self.setContextMenuPolicy(Qt.PreventContextMenu)
# Drop-down list for Windowing button on toolbar
self.button_windowing = QToolButton()
self.button_windowing.setMenu(self.action_handler.menu_windowing)
self.button_windowing.setPopupMode(QToolButton.InstantPopup)
self.button_windowing.setIcon(self.action_handler.icon_windowing)
# Drop-down list for Export button on toolbar
self.button_export = QToolButton()
self.button_export.setMenu(self.action_handler.menu_export)
self.button_export.setPopupMode(QToolButton.InstantPopup)
self.button_export.setIcon(self.action_handler.icon_export)
# Spacer for the toolbar
spacer = QWidget()
spacer.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
spacer.setFocusPolicy(Qt.NoFocus)
# Add actions to toolbar
self.addAction(self.action_handler.action_open)
self.addSeparator()
self.addAction(self.action_handler.action_zoom_out)
self.addAction(self.action_handler.action_zoom_in)
self.addSeparator()
self.addWidget(self.button_windowing)
self.addSeparator()
self.addAction(self.action_handler.action_transect)
self.addSeparator()
self.addAction(self.action_handler.action_one_view)
self.addAction(self.action_handler.action_four_views)
self.addAction(self.action_handler.action_show_cut_lines)
self.addSeparator()
self.addAction(self.action_handler.action_image_fusion)
self.addSeparator()
self.addAction(self.action_handler.action_add_ons)
self.addWidget(spacer)
self.addWidget(self.button_export)
self.addAction(self.action_handler.action_save_as_anonymous)
if self.patient_dict_container.has_modality('rtss'):
self.addAction(self.action_handler.action_save_structure)
def __init__(self):
self.dvh_data = None
# Load test DICOM files
desired_path = Path.cwd().joinpath('test', 'testdata')
selected_files = find_DICOM_files(desired_path)
file_path = os.path.dirname(os.path.commonprefix(selected_files))
read_data_dict, file_names_dict = \
ImageLoading.get_datasets(selected_files)
# Create patient dict container object
self.patient_dict_container = PatientDictContainer()
self.patient_dict_container.clear()
self.patient_dict_container.set_initial_values(file_path,
read_data_dict,
file_names_dict)
def create_fused_model(old_images, new_image):
"""
Performs the image fusion and stores fusion information.
Args:
old_images(sitk): Image set from Primary/Fixed Image
new_image(sitk): Image set from the Secondary/Moving Image
"""
patient_dict_container = PatientDictContainer()
fused_image = register_images(old_images, new_image)
patient_dict_container.set("fused_images", fused_image)
if fused_image[2]:
combined_affine = convert_composite_to_affine_transform(fused_image[1])
affine_matrix = convert_combined_affine_to_matrix(combined_affine)
write_transform_to_dcm(affine_matrix)
def create_new_rtstruct(cls, progress_callback):
"""
Generates a new RTSS and edits the patient dict container. Used
for batch processing.
"""
# Get common directory
patient_dict_container = PatientDictContainer()
file_path = patient_dict_container.filepaths.values()
file_path = Path(os.path.commonpath(file_path))
# Get new RT Struct file path
file_path = str(file_path.joinpath("rtss.dcm"))
# Create RT Struct file
progress_callback.emit(("Generating RT Structure Set", 60))
ct_uid_list = ImageLoading.get_image_uid_list(
patient_dict_container.dataset)
ds = ROI.create_initial_rtss_from_ct(patient_dict_container.dataset[0],
file_path, ct_uid_list)
ds.save_as(file_path)
# Add RT Struct file path to patient dict container
patient_dict_container.filepaths['rtss'] = file_path
filepaths = patient_dict_container.filepaths
# Add RT Struct dataset to patient dict container
patient_dict_container.dataset['rtss'] = ds
dataset = patient_dict_container.dataset
# Set some patient dict container attributes
patient_dict_container.set("file_rtss", filepaths['rtss'])
patient_dict_container.set("dataset_rtss", dataset['rtss'])
dicom_tree_rtss = DicomTree(filepaths['rtss'])
patient_dict_container.set("dict_dicom_tree_rtss",
dicom_tree_rtss.dict)
dict_pixluts = ImageLoading.get_pixluts(patient_dict_container.dataset)
patient_dict_container.set("pixluts", dict_pixluts)
rois = ImageLoading.get_roi_info(ds)
patient_dict_container.set("rois", rois)
patient_dict_container.set("selected_rois", [])
patient_dict_container.set("dict_polygons_axial", {})
patient_dict_container.set("rtss_modified", True)
def dvh2rtdose(dict_dvh):
"""
Export dvh data to RT DOSE file.
:param dict_dvh: A dictionary of DVH {ROINumber: DVH}
:param patient_id: Patient Identifier
"""
# Create DVH sequence
dvh_sequence = Sequence([])
# Add DVHs to the sequence
for ds in dict_dvh:
# Create new DVH dataset
new_ds = Dataset()
# Add attributes
new_ds.add_new(Tag("DVHType"), "CS", dict_dvh[ds].dvh_type.upper())
new_ds.add_new(Tag("DoseUnits"), "CS", dict_dvh[ds].dose_units.upper())
new_ds.add_new(Tag("DoseType"), "CS", "PHYSICAL")
new_ds.add_new(Tag("DVHDoseScaling"), "DS", "1.0")
new_ds.add_new(Tag("DVHVolumeUnits"), "CS",
dict_dvh[ds].volume_units.upper())
new_ds.add_new(Tag("DVHNumberOfBins"), "IS", len(dict_dvh[ds].bins))
# Calculate and add DVH data
dvh_data = []
for i in range(len(dict_dvh[ds].counts)):
dvh_data.append(str(dict_dvh[ds].bins[1]))
dvh_data.append(str(dict_dvh[ds].counts[i]))
new_ds.add_new(Tag("DVHData"), "DS", dvh_data)
# Reference ROI sequence dataset/sequence
referenced_roi_sequence = Dataset()
referenced_roi_sequence.add_new(Tag("DVHROIContributionType"), "CS",
"INCLUDED")
referenced_roi_sequence.add_new(Tag("ReferencedROINumber"), "IS", ds)
new_ds.add_new(Tag("DVHReferencedROISequence"), "SQ",
Sequence([referenced_roi_sequence]))
# Add new DVH dataset to DVH sequences
dvh_sequence.append(new_ds)
# Save new RT DOSE
patient_dict_container = PatientDictContainer()
patient_dict_container.dataset['rtdose'].DVHSequence = dvh_sequence
path = patient_dict_container.filepaths['rtdose']
patient_dict_container.dataset['rtdose'].save_as(path)
