Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.

ABSTRACT:

Purpose

To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.

Method

A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and qu

SUBMITTER: Belue MJ 

PROVIDER: S-EPMC10842312 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature