  In current clinical practice, noisy and artifact-ridden weekly cone-beam
computed tomography (CBCT) images are only used for patient setup during
radiotherapy. Treatment planning is done once at the beginning of the treatment
using high-quality planning CT (pCT) images and manual contours for
organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can
be improved while simultaneously segmenting OAR structures, this can provide
critical information for adapting radiotherapy mid-treatment as well as for
deriving biomarkers for treatment response. Using a novel physics-based data
augmentation strategy, we synthesize a large dataset of perfectly/inherently
registered planning CT and synthetic-CBCT pairs for locally advanced lung
cancer patient cohort, which are then used in a multitask 3D deep learning
framework to simultaneously segment and translate real weekly CBCT images to
high-quality planning CT-like images. We compared the synthetic CT and OAR
segmentations generated by the model to real planning CT and manual OAR
segmentations and showed promising results. The real week 1 (baseline) CBCT
images which had an average MAE of 162.77 HU compared to pCT images are
translated to synthetic CT images that exhibit a drastically improved average
MAE 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 allow
clinicians to adjust treatment plans using only the routine low-quality CBCT
images, potentially improving patient outcomes. Our code, data, and pre-trained
models will be made available via our physics-based data augmentation library,
Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.
