# We're published! Please check out the Technical Note here: https://www.sciencedirect.com/science/article/abs/pii/S1879850021000485 and reference this work if you find it useful
### DOI:https://doi.org/10.1016/j.prro.2021.02.003
## This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures
## Installation guide
pip install DicomRTTool
### Highly recommend to go through the jupyter notebook in the Examples folder and to read the Wiki
### Quick use guide
from DicomRTTool.ReaderWriter import DicomReaderWriter, ROIAssociationClass
Dicom_path = r'.some_path_to_dicom'
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True)
Dicom_reader.walk_through_folders(Dicom_path) # This will parse through all DICOM present in the folder and subfolders
all_rois = Dicom_reader.return_rois(print_rois=True) # Return a list of all rois present
Contour_names = ['tumor'] # Define what rois you want
associations = [ROIAssociationClass('tumor', ['tumor_mr', 'tumor_ct'])] # Any list of roi associations
Dicom_reader.set_contour_names_and_assocations(contour_names=Contour_names, associations=associations)
Dicom_reader.get_images_and_mask()
image_numpy = Dicom_reader.ArrayDicom
mask_numpy = Dicom_reader.mask
image_sitk_handle = Dicom_reader.dicom_handle
mask_sitk_handle = Dicom_reader.annotation_handle
### Other interesting additions
### Adding information to the Dicom_reader.series_instances_dictionary
from DicomRTTool.ReaderWriter import Tag
plan_pydicom_string_keys = {"MyNamedRTPlan": Tag((0x300a, 0x002))}
image_sitk_string_keys = {"MyPatientName": "0010|0010"}
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True, plan_pydicom_string_keys=plan_pydicom_string_keys, image_sitk_string_keys=image_sitk_string_keys)
##### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
###### Ring update allows for multiple rings to be represented correctly
#### Works on oblique images for masks and predictions*
Raw data
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