Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

• URL: http://arxiv.org/abs/2105.08163v1
• Date: Mon, 17 May 2021 21:06:14 GMT
• Title: Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
• Authors: Eric Z. Chen, Yongquan Ye, Xiao Chen, Jingyuan Lyu, Zhongqi Zhang, Yichen Hu, Terrence Chen, Jian Xu, and Shanhui Sun
• Abstract summary: Multi-flip-angle (FA) and multi-echoLEX GRE method (MULTIP MRI) has been developed to simultaneously acquire multiple parametric images with just one single scan. We propose a deep learning framework for undersampled 3D MRI data reconstruction. The proposed deep learning method shows good performance in image quality and reconstruction time.
• Score: 7.85035197356331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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