Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

TL;DR

This paper introduces a deep learning framework to accelerate 3D MULTIPLEX MRI reconstruction, addressing long scan times and large data volumes, and demonstrating improved image quality and efficiency.

Contribution

It presents the first deep learning-based method specifically designed for 3D MULTIPLEX MRI data reconstruction, improving speed and quality.

Findings

Enhanced image quality in reconstructed MRI images

Reduced reconstruction time compared to traditional methods

Effective handling of large 3D multi-contrast data sets

Abstract

Multi-contrast MRI images provide complementary contrast information about the characteristics of anatomical structures and are commonly used in clinical practice. Recently, a multi-flip-angle (FA) and multi-echo GRE method (MULTIPLEX MRI) has been developed to simultaneously acquire multiple parametric images with just one single scan. However, it poses two challenges for MULTIPLEX to be used in the 3D high-resolution setting: a relatively long scan time and the huge amount of 3D multi-contrast data for reconstruction. Currently, no DL based method has been proposed for 3D MULTIPLEX data reconstruction. We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI. The proposed deep learning method shows good performance in image quality and reconstruction time.