IEEE Trans Med Imaging. 2021 Sep;40(9):2477-2486. doi: 10.1109/TMI.2021.3081013. Epub 2021 Aug 31.
Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.
基于模型的重建方法已经成为经典傅里叶 MRI 技术的有力替代方法，主要是因为它们能够明确建模（并且因此可能克服）中等场不均匀性，从非笛卡尔采样中简化重建，甚至允许使用定制的非傅里叶编码方法。然而，由于此类设置中的相应正向模型不再具有基于直接傅里叶变换的实现，因此它们在这些情况下的应用通常会带来大量的计算成本增加。本文介绍了一种算法框架，旨在降低基于模型的 MRI 重建任务相关的计算负担。关键创新在于对这些模型的相应正向算子进行策略稀疏化，从而得到正向模型（及其伴随算子）的近似值，这些近似值可以应用低计算复杂度。这使得这些重建任务的流行的一阶迭代重建方法的整体应用可以降低计算复杂度。在合成和实验数据上获得的计算结果说明了该方法的可行性和效率。
