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2D sparse sampling algorithm for ND Fredholm equations with applications to NMR relaxometry

机译:ND Fredholm方程的2D稀疏采样算法及其在NMR弛豫分析中的应用

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摘要

In [1], Cloninger, Czaja, Bai, and Basser developed an algorithm for compressive sampling based data acquisition for the solution of 2D Fredholm equations. We extend the algorithm to N dimensional data, by randomly sampling in 2 dimensions and fully sampling in the remaining N-2 dimensions. This new algorithm has direct applications to 3-dimensional nuclear magnetic resonance relaxometry and related experiments, such as T-D-T or T-T-T. In these experiments, the first two parameters are time-consuming to acquire, so sparse sampling in the first two parameters can provide significant experimental time savings, while compressive sampling is unnecessary in the third parameter.
机译:在[1]中,Cloninger,Czaja,Bai和Basser开发了一种基于压缩采样的数据采集算法,用于求解2D Fredholm方程。通过在2维中随机采样并在其余N-2维中完全采样,我们将算法扩展到N维数据。该新算法可直接应用于三维核磁共振弛豫法和相关实验,例如T-D-T或T-T-T。在这些实验中,获取前两个参数很费时,因此前两个参数中的稀疏采样可以节省大量的实验时间,而第三个参数中不需要压缩采样。

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