Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.
Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS Shenzhen 518055, China.
Med Image Anal. 2020 Aug;64:101717. doi: 10.1016/j.media.2020.101717. Epub 2020 May 21.
Although recent deep learning methodology has shown promising performance in fast imaging, the network needs to be retrained for specific sampling patterns and ratios. Therefore, how to explore the network as a general prior and leverage it into the observation constraint flexibly is urgent. In this work, we present a multi-channel enhanced Deep Mean-Shift Prior (MEDMSP) to address the highly under-sampled magnetic resonance imaging reconstruction problem. By extending the naive DMSP via integration of multi-model aggregation and multi-channel network learning, a high-dimensional embedding network derived prior is formed. Then, we apply the learned prior to single-channel image reconstruction via variable augmentation technique. The resulting model is tackled by proximal gradient descent and alternative iteration. Experimental results under various sampling trajectories and acceleration factors consistently demonstrated the superiority of the proposed prior.
尽管最近的深度学习方法在快速成像方面表现出了很有前景的性能，但网络仍需要针对特定的采样模式和比例进行重新训练。因此，如何将网络探索为一种通用的先验知识，并灵活地将其应用于观测约束，这是一个紧迫的问题。在这项工作中，我们提出了一种多通道增强的深度均值漂移先验（MEDMSP）方法，以解决高度欠采样的磁共振成像重建问题。通过扩展朴素的 DMSP，通过多模型聚合和多通道网络学习的集成，形成了一个高维嵌入网络衍生的先验知识。然后，我们通过变量增强技术将学习到的先验知识应用于单通道图像重建。所得到的模型通过近端梯度下降和交替迭代来解决。在各种采样轨迹和加速因子下的实验结果一致表明了所提出的先验知识的优越性。
