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In current clinical practice, noisy and artifact-ridden weekly cone-beamcomputed tomography (CBCT) images are only used for patient setup duringradiotherapy. Treatment planning is done once at the beginning of the treatmentusing high-quality planning CT (pCT) images and manual contours fororgans-at-risk (OARs) structures. If the quality of the weekly CBCT images canbe improved while simultaneously segmenting OAR structures, this can providecritical information for adapting radiotherapy mid-treatment as well as forderiving biomarkers for treatment response. Using a novel physics-based dataaugmentation strategy, we synthesize a large dataset of perfectly/inherentlyregistered planning CT and synthetic-CBCT pairs for locally advanced lungcancer patient cohort, which are then used in a multitask 3D deep learningframework to simultaneously segment and translate real weekly CBCT images tohigh-quality planning CT-like images. We compared the synthetic CT and OARsegmentations generated by the model to real planning CT and manual OARsegmentations and showed promising results. The real week 1 (baseline) CBCTimages which had an average MAE of 162.77 HU compared to pCT images aretranslated to synthetic CT images that exhibit a drastically improved averageMAE of 29.31 HU and average structural similarity of 92% with the pCT images.The average DICE scores of the 3D organs-at-risk segmentations are: lungs 0.96,heart 0.88, spinal cord 0.83 and esophagus 0.66. This approach could allowclinicians to adjust treatment plans using only the routine low-quality CBCTimages, potentially improving patient outcomes. Our code, data, and pre-trainedmodels will be made available via our physics-based data augmentation library,Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation