Submillimeter MR fingerprinting using deep learning-based tissue quantification.

ABSTRACT:

Purpose

To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach.

Methods

A rapid and high-resolution MR fingerprinting technique was developed for brain T₁ and T₂ quantification. The 2D acquisition was performed using a FISP-based MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning-based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects,

SUBMITTER: Fang Z 

PROVIDER: S-EPMC8210473 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature