Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.

ABSTRACT:

Rationale and objectives

This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. The goal was to provide accurate Hounsfield unit (HU) data for dose calculation to enable MRI simulation and adaptive radiation therapy (ART) using CBCT or MRI. We also compared the performance and benefits of StarGAN to the commonly used CycleGAN.

Materials and methods

StarGAN and CycleGAN were employed in this study. The dataset comprised 53 cases of pelvic cancer. Evaluation involved qualitative and quantitative analyses, focusing on synthetic image quality and dose distribution calculation.

Results

For sCT generated from

SUBMITTER: Wongtrakool P 

PROVIDER: S-EPMC11796232 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature