  Purpose: 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. Methods: 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. Results: 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. Conclusions:
We demonstrate an approach to translate artifact-ridden CBCT images to high
quality synthetic CT images while simultaneously generating good quality
segmentation masks for different organs-at-risk. This approach could allow
clinicians to adjust treatment plans using only the routine low-quality CBCT
images, potentially improving patient outcomes.
