A divide-and-conquer approach to compressed sensing MRI
作者: Liyan SunZhiwen FanXinghao DingCongbo CaiYue HuangJohn Paisley
作者单位: 1Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, China
2Department of Electronic Science, Xiamen University, Xiamen, China
3Department of Electrical Engineering, Columbia University, New York, NY, USA
刊名: Magnetic Resonance Imaging, 2019, Vol.63 , pp.37-48
来源数据库: Elsevier Journal
DOI: 10.1016/j.mri.2019.06.014
关键词: Compressed sensingMagnetic resonance imagingDivide-and-conquer
原始语种摘要: Abstract(#br)Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often neglected during reconstruction. As a result, more densely sampled low frequency information tends to dominate penalization schemes for reconstructing MRI at the expense of high frequency details. In this paper, we propose a new framework for CS-MRI inversion in which we decompose the observed k-space data into “subspaces” via sets of filters in a lossless way, and reconstruct the images in these various spaces individually using off-the-shelf algorithms. We then...
全文获取路径: Elsevier  (合作)
影响因子:2.06 (2012)

  • compressed 侧扁的
  • reconstruction 复原
  • sensing 感觉
  • lossless 无损耗
  • framework 构架
  • decompose 分解
  • MRI Machine-Readable Information
  • preserving 保藏
  • distributed 分布的
  • details 细节