Unsupervised Bayesian generation of synthetic CT from CBCT using patient-specific score-based prior.

ABSTRACT:

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 q

SUBMITTER: Peng J 

PROVIDER: S-EPMC12359064 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature