Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning.

ABSTRACT:

Purpose

To develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.

Methods

Eighteen subjects were imaged using an MR Multitasking sequence to generate reference T₁ and T₂ maps in the brain. Conventional contrast-weighted images consisting of T₁ MPRAGE, T₁ GRE, and T₂ FLAIR were acquired as input images. A U-Net-based neural network was trained to estimate T₁ and T₂ maps simultaneously from the contrast-weighted images. Six-fold cross-validation was performed to compare the network outputs with the MR Multitasking references.

Results

The deep-learning T₁ /T₂ maps were comparable with the references, and

SUBMITTER: Qiu S 

PROVIDER: S-EPMC8616775 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature