Compressed sensing MRI with combined sparsifying transforms and smoothed l0 norm minimization
Compressed sensing MRI with combined sparsifying transforms and smoothed l0 norm minimization
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DOI：
10.1109/icassp.2010.5495174
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发表时间：
2010-03
期刊：
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通讯作者：
X. Qu;Xue Cao;D. Guo;Changwei Hu;Zhong Chen
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文献类型：
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作者：
X. Qu;Xue Cao;D. Guo;Changwei Hu;Zhong Chen
对k空间进行欠采样是提高磁共振成像（MRI）速度的有效方法。最近出现的压缩传感MRI显示出有希望的结果。然而，他们中的大多数只加强在单一的变换，如总变分，小波等图像的稀疏性在本文中，基于基追踪的原则，我们提出了一个新的框架，结合联合收割机稀疏变换在压缩感知MRI。每一种变换都能有效地表示另一种变换所不能表示的特定特征。这个框架是通过最先进的平滑l0范数过完备稀疏分解实现的。仿真结果表明，与单一稀疏化变换相比，该方法可以提高图像质量。
Undersampling the k-space is an efficient way to speed up the magnetic resonance imaging (MRI). Recently emerged compressed sensing MRI shows promising results. However, most of them only enforce the sparsity of images in single transform, e.g. total variation, wavelet, etc. In this paper, based on the principle of basis pursuit, we propose a new framework to combine sparsifying transforms in compressed sensing MRI. Each transform can efficiently represent specific feature that the other can not. This framework is implemented via the state-of-art smoothed l0 norm in overcomplete sparse decomposition. Simulation results demonstrate that the proposed method can improve image quality when comparing to single sparsifying transform.