The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

中文翻译：

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无需先验即可根据稀疏磁共振成像径向数据进行非迭代图像重建。

使用欠采样k空间数据进行图像重建的最新方法是基于压缩感知的。它们是迭代算法，可以优化具有空间和/或时间约束的目标函数。本文提出了一种非迭代算法来估计未测量的数据，然后使用有效的滤波反投影算法重建图像。病人磁共振成像研究证明了该方法的可行性。还将该提议的方法与使用总变量优化范数的最新的迭代压缩感测图像重建方法进行了比较。