Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.

ABSTRACT:

Background

Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS (VirtuT2w) images from routine multiparametric breast MRI images.

Methods

This IRB-approved, retrospective study included 914 breast MRI examinations from January 2017 to June 2020. The dataset was divided into training (n = 665), validation (n = 74), and test sets (n = 175). The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. Quantitative metrics were used to evaluate the different in

SUBMITTER: Liebert A 

PROVIDER: S-EPMC12048370 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature