Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.

ABSTRACT:

Purpose

This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.

Methods

Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and e

SUBMITTER: Zhu J 

PROVIDER: S-EPMC10314402 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature