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非均匀稀疏采样多维 NMR 数据的网格化和快速傅里叶变换。

Gridding and fast Fourier transformation on non-uniformly sparse sampled multidimensional NMR data.

机构信息

State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, The Chinese Academy of Sciences, Wuhan 430071, China.

出版信息

J Magn Reson. 2010 May;204(1):165-8. doi: 10.1016/j.jmr.2010.02.009. Epub 2010 Feb 20.

Abstract

For multidimensional NMR method, indirect dimensional non-uniform sparse sampling can dramatically shorten acquisition time of the experiments. However, the non-uniformly sampled NMR data cannot be processed directly using fast Fourier transform (FFT). We show that the non-uniformly sampled NMR data can be reconstructed to Cartesian grid with the gridding method that has been wide applied in MRI, and sequentially be processed using FFT. The proposed gridding-FFT (GFFT) method increases the processing speed sharply compared with the previously proposed non-uniform Fourier Transform, and may speed up application of the non-uniform sparse sampling approaches.

摘要

对于多维 NMR 方法,间接维度非均匀稀疏采样可以显著缩短实验的采集时间。然而,非均匀采样的 NMR 数据不能直接使用快速傅里叶变换(FFT)进行处理。我们表明,可以使用已经广泛应用于 MRI 的网格化方法将非均匀采样的 NMR 数据重建到笛卡尔网格上,并使用 FFT 进行顺序处理。与之前提出的非均匀傅里叶变换相比,所提出的网格化-FFT(GFFT)方法大大提高了处理速度,并且可能加速非均匀稀疏采样方法的应用。

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