A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction

Abstract

In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimization-based models suffer several limitations: strict assumption of shared sparse support, time-consuming optimization and "shallow" models with difficulties in encoding the rich patterns hiding in massive MRI data. In this paper, we propose the first deep learning model for multi-contrast MRI reconstruction. We achieve information sharing through feature sharing units, which significantly reduces the number of parameters. The feature sharing unit is combined with a data fidelity unit to comprise an inference block. These inference blocks are cascaded with dense connections, which allows for information transmission across different depths of the network efficiently. Our extensive experiments on various multi-contrast MRI datasets show that proposed model outperforms both state-of-the-art single-contrast and multi-contrast MRI methods in accuracy and efficiency. We show the improved reconstruction quality can bring great benefits for the later medical image analysis stage. Furthermore, the robustness of the proposed model to the non-registration environment shows its potential in real MRI applications.

arXiv comment13 pages, 16 figures, 3 tables

References in corpus (4)

• Opening the Black Box of Deep Neural Networks via Information
• Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
• Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
• An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios

Cited by in corpus (4)

• Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network
• A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
• Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
• A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications