  Background: Daily or weekly cone-beam computed tomography (CBCT) scans are
commonly used for accurate patient positioning during the image-guided
radiotherapy (IGRT) process, making it an ideal option for adaptive
radiotherapy (ART) replanning. However, the presence of severe artifacts and
inaccurate Hounsfield unit (HU) values prevent its use for quantitative
applications such as organ segmentation and dose calculation. To enable the
clinical practice of online ART, it is crucial to obtain CBCT scans with a
quality comparable to that of a CT scan. Purpose: This work aims to develop a
conditional diffusion model to perform image translation from the CBCT to the
CT domain for the image quality improvement of CBCT. Methods: The proposed
method is a conditional denoising diffusion probabilistic model (DDPM) that
utilizes a time-embedded U-net architecture with residual and attention blocks
to gradually transform standard Gaussian noise to the target CT distribution
conditioned on the CBCT. The model was trained on deformed planning CT (dpCT)
and CBCT image pairs, and its feasibility was verified in brain patient study
and head-and-neck (H&N) patient study. The performance of the proposed
algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise
ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated
synthetic CT (sCT) samples. The proposed method was also compared to four other
diffusion model-based sCT generation methods. Conclusions: The proposed
conditional DDPM method can generate sCT from CBCT with accurate HU numbers and
reduced artifacts, enabling accurate CBCT-based organ segmentation and dose
calculation for online ART.
