IEEE Trans Image Process. 2019 Dec;28(12):6141-6153. doi: 10.1109/TIP.2019.2925288. Epub 2019 Jul 9.
Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a strict assumption of shared sparse support, time-consuming optimization, and "shallow" models with difficulties in encoding the patterns contained in massive MRI data. In this paper, we propose the first deep learning model for multi-contrast CS-MRI reconstruction. We achieve information sharing through feature sharing units, which significantly reduces the number of model parameters. The feature sharing unit combines with a data fidelity unit to comprise an inference block, which are then cascaded with dense connections, allowing for efficient information transmission across different depths of the network. Experiments on various multi-contrast MRI datasets show that the proposed model outperforms both state-of-the-art single-contrast and multi-contrast MRI methods in accuracy and efficiency. We demonstrate that improved reconstruction quality can bring benefits to subsequent medical image analysis. Furthermore, the robustness of the proposed model to misregistration shows its potential in real MRI applications.
压缩感知（CS）理论可以通过在每个对比度内采集更少的测量值来加速多对比度磁共振成像（MRI）。然而，传统的基于优化的重建模型存在几个限制，包括对共享稀疏支撑的严格假设、耗时的优化以及在编码大量 MRI 数据中包含的模式方面存在困难的“浅层”模型。在本文中，我们提出了第一个用于多对比度 CS-MRI 重建的深度学习模型。我们通过特征共享单元实现信息共享，这大大减少了模型参数的数量。特征共享单元与数据保真度单元相结合构成推理块，然后通过密集连接级联，允许在网络的不同深度之间高效地传输信息。在各种多对比度 MRI 数据集上的实验表明，所提出的模型在准确性和效率方面均优于最先进的单对比度和多对比度 MRI 方法。我们证明了改进的重建质量可以为后续的医学图像分析带来益处。此外，所提出的模型对配准错误的鲁棒性表明了其在实际 MRI 应用中的潜力。
