Sparse MRI: The application of compressed sensing for rapid MR imaging
Sparse MRI: The application of compressed sensing for rapid MR imaging
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DOI：
10.1002/mrm.21391
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发表时间：
2007-12-01
影响因子：
3.3
通讯作者：
Pauly, John M.
中科院分区：
文献类型：
--
作者：
Lustig, Michael;Donoho, David;Pauly, John M.
利用MR图像中隐含的稀疏性来显著欠采样k空间。一些MR图像，如血管造影片已经稀疏的像素表示，其他更复杂的图像有一个稀疏的表示在某些变换域，例如，在空间有限差分或其小波系数。根据最近发展的压缩感知的数学理论，具有稀疏表示的图像可以从随机欠采样的k空间数据中恢复，只要使用适当的非线性恢复方案。直观地说，由于随机欠采样而产生的伪影添加为类似噪声的干扰。在稀疏变换域中，重要系数在干扰之上突出。一个非线性阈值方案可以恢复稀疏系数，有效地恢复图像本身。本文提出了实用的非相干欠采样方案，并从混叠干扰的角度对其进行了分析。相位编码的伪随机变密度欠采样引入了非相干性。通过最小化变换图像的l（1）范数来执行重建，受到数据保真度约束。实例证明了多层快速自旋回波脑成像和3D对比增强血管造影的空间分辨率提高和采集加速。
The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain-for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed-sensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the l(1) norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spinecho brain imaging and 3D contrast enhanced angiography.