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GENERALIZED K-T BLAST AND K-T SENSE USING FOCUSS

机译:使用焦点的广义K-T Blast和K-T感应

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According to the recent theory of compressed sensing, accurate reconstruction is possible even from data samples dramatically smaller than Nyquist sampling limit as long as the unknown image is sparse. In MRI, dynamic imaging such as cardiac cine may be a nice application of the compressed sensing theory since it requires significant reduction of data acquisition time whereas the periodic motions are usually sparse in spectral domain. The main contribution of this paper is to show that a sparse reconstruction method called the FOCal Underdetermined System Solver (FOCUSS) is a very effective compressed sensing reconstruction algorithm by exploiting the sparsity in spectral domain. Furthermore, our analysis reveals that celebrated k-t BLAST and k-t SENSE are special cases of our algorithm; hence our algorithm outperforms them in general situations.
机译:根据最近的压缩感测理论,即使从数据样本大于奈奎斯特采样限制,即使从数据样本也是如此,只要未知图像稀疏,即使从奈奎斯特采样限制的数据样本也是可能的。在MRI中,诸如心脏的动态成像可以是压缩感测理论的很好应用,因为它需要对数据采集时间的显着降低,而周期性运动通常在光谱域中稀疏。本文的主要贡献是表明,通过利用光谱域中的稀疏性,一种称为焦点未确定系统求解器(Focuss)的稀疏重建方法是非常有效的压缩检测重建算法。此外,我们的分析显示,庆祝的K-T Blast和K-T感觉是我们算法的特殊情况;因此,我们的算法在一般情况下优于它们。

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