Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network.

ABSTRACT:

Purpose

Stack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness.

Methods

A 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mea

SUBMITTER: Gao C 

PROVIDER: S-EPMC10163826 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature