# Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

arXiv: https://arxiv.org/abs/2105.08163 · HTML: https://mlsanity.com/paper/2105.08163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 1 of 1)

- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)

## Built on by (most cited first, 1 of 1)

- [Computationally Efficient 3D MRI Reconstruction with Adaptive MLP](https://mlsanity.com/paper/2301.08868/) (2023)

## Abstract (arXiv)

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.