CBCT-based synthetic CT image generation using a diffusion model for CBCT-guided lung radiotherapy.

ABSTRACT:

Background

Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process.

Purpose

While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework.

Methods

Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to sy

SUBMITTER: Chen X 

PROVIDER: S-EPMC11651384 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature