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.

本文提出了一种基于深度学习的多对比度 MRI 重建模型，通过特征共享单元和密集连接单元进行信息传输和参数优化，实现了对大规模 MRI 数据集的高效重建，并在精确度和效率方面优于现有的单对比度和多对比度 MRI 方法。