We propose a new fast algorithm for simultaneous recovery of the coil sensitivities and the magnetization image from incomplete Fourier measurements in parallel MRI. Our approach is based on suitable parameter models for both, the magnetization image and the sensitivities. The derived MOCCA algorithm provides perfect reconstruction results if the model assumptions are satisfied. Moreover, it has low computational complexity and achieves very good performance for incomplete MRI data. We present a complete mathematical analysis of the proposed reconstruction method. Most importantly, MOCCA leads to a better understanding of the connections between subspace methods and sensitivity modeling which will provide us the with the opportunity to improve also existing algorithms as ESPIRiT.

翻译：我们提出了一种新的快速算法，用于从并行磁共振成像的不完全傅里叶测量数据中同时恢复线圈灵敏度与磁化图像。该方法基于针对磁化图像和线圈灵敏度的合适参数模型。当模型假设满足时，推导出的MOCCA算法可实现完美的重建结果。此外，该算法计算复杂度低，且对不完整MRI数据表现出优异的性能。我们对所提出的重建方法进行了完整的数学分析。最重要的是，MOCCA算法有助于更深入理解子空间方法与灵敏度建模之间的关联，这为我们改进现有算法（如ESPIRiT）提供了契机。