CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.

ABSTRACT:

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 distribution for the image quality improvement of CBCT.

Methods

The proposed method is a conditional denois

SUBMITTER: Peng J 

PROVIDER: S-EPMC11747767 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature