Encoding sampling pattern for robust and generalized MRI reconstruction

Against the magnetic resonance imaging (MRI) reconstruction task, current deep learning based methods have achieved promising performance. Nevertheless, most of them are confronted with two main problems: (1) For most current MRI reconstruction methods, the down-sampling pattern is generally preset in advance, which makes it hard to flexibly handle the complicated real scenarios where the training data and the testing data are obtained under different sampling settings, thus constraining the model generalization capability. (2) They have not fully incorporated the physical imaging mechanism between the down-sampling pattern estimation and high-resolution MRI reconstruction into deep network design for this specific task. To alleviate these issues, we propose a model-driven MRI reconstruction network called MXNet, which considers the relationship between the undersampling pattern and imaging by encoding the mask into the network. Specifically, based on the MR physical imaging process, we first jointly optimize the down-sampling pattern and MRI reconstruction network. Then, based on the proposed optimization algorithm and the deep unfolding technique, we correspondingly construct the deep network where the physical imaging mechanism for MRI reconstruction is fully embedded into the entire learning process. Based on different settings between training data and testing data, with both consistent and inconsistent down-sampling patterns, extensive experiments comprehensively substantiate the effectiveness of our proposed MXNet in detail reconstruction as well as its fine generality. Moreover, we provide detailed model analysis and validate that our proposed framework shows fine generality and it can still accomplish superior performance when the downsampling mask is accurately available. The code is available at https://github.com/sunliyangna0705/MXNet.