IEEE Trans Med Imaging. 2018 Jan;37(1):251-261. doi: 10.1109/TMI.2017.2746086. Epub 2017 Aug 29.
The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.
压缩感知和并行成像（CS-PI）的集成近年来在加速磁共振（MR）成像方面越来越受欢迎。其中，由于其能够稳健地处理灵敏度信息，无校准技术表现出了令人鼓舞的性能。不幸的是，现有的无校准方法仅探索了通过直接分析变换投影的联合稀疏性。为了进一步利用联合稀疏性并提高重建准确性，本文提出通过将并行 MR 成像问题建模为具有范数约束数据保真度、Frobenius 范数强制稀疏表示误差和混合范数触发跨多通道的联合稀疏性的 - - 最小化目标，来学习无校准并行 MR 成像（LINDBERG）的联合稀疏码。开发了一种相应的算法来交替更新稀疏表示、敏感编码图像和 K 空间数据。然后，将所有通道图像的平方和的平方根作为最终图像。在物理体模和体内数据集上的实验结果表明，所提出的方法与最先进的 CS-PI 重建方法相当，甚至更优。具体来说，LINDBERG 在从高度欠采样的多通道测量中重建 MR 图像时，具有强大的抑制噪声和伪影的能力。
