Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy.

ABSTRACT: Although MR-guided radiotherapy (MRgRT) is advancing rapidly, generating accurate synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural networks require large dataset of precisely co-registered CT and MRI pairs that are difficult to obtain due to respiration and peristalsis. Here, we propose a method to generate sCT based on deep learning training with weakly paired CT and MR images acquired from an MRgRT system using a cycle-consistent GAN (CycleGAN) framework that allows the unpaired image-to-image translation in abdomen and thorax. Data from 90 cancer patients who underwent MRgRT were retrospectively used. CT images of the patients were aligned to the corresponding MR images using deformable registration, and the deformed CT (dCT) and MRI pairs were use

SUBMITTER: Kang SK 

PROVIDER: S-EPMC8316520 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature