  Background: Cone-beam computed tomography (CBCT) scans, performed
fractionally (e.g., daily or weekly), are widely utilized for patient alignment
in the image-guided radiotherapy (IGRT) process, thereby making it a potential
imaging modality for the implementation of adaptive radiotherapy (ART)
protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit
(HU) values hinder their application in quantitative tasks such as target and
organ segmentations and dose calculation. Therefore, acquiring CT-quality
images from the CBCT scans is essential to implement online ART in clinical
settings.
  Purpose: This work aims to develop an unsupervised learning method using the
patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation
to improve the image quality of CBCT.
  Methods: The proposed method is in an unsupervised framework that utilizes a
patient-specific score-based model as the image prior alongside a customized
total variation (TV) regularization to enforce coherence across different
transverse slices. The score-based model is unconditionally trained using the
same patient's planning CT (pCT) images to characterize the manifold of
CT-quality images and capture the unique anatomical information of the specific
patient. The efficacy of the proposed method was assessed on images from
anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and
lung cancer. The performance of the proposed CBCT correction method was
evaluated using quantitative metrics including mean absolute error (MAE), peak
signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC).
Additionally, the proposed algorithm was benchmarked against two other
unsupervised diffusion model-based CBCT correction algorithms.
