Sun Mei, Tao Jinxu, Ye Zhongfu, Qiu Bensheng, Xu Jinzhang, Xi Changfeng
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui 230027, China.
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China.
Curr Med Imaging Rev. 2019;15(3):281-291. doi: 10.2174/1573405614666180130151333.
In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction.
Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction.
Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation.
RESULTS & CONCLUSION: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.
为了克服长扫描时间的限制，压缩感知（CS）技术利用图像在某些变换域中的稀疏性来减少采集的数据量。因此，CS已广泛应用于磁共振成像（MRI）重建。
盲压缩感知能够从高度欠采样的测量中成功恢复图像，这是因为对未知变换基先验进行了数据驱动的自适应。此外，基于分析的盲压缩感知通常比基于合成的盲压缩感知能以更少的时间实现更高效的信号重建。最近，一些实验表明非局部低秩特性有能力保留用于MRI重建的图像细节。
在此，我们专注于基于分析的盲压缩感知，并将其与额外的非局部低秩约束相结合，以便从更少的测量中获得更好的MR图像。我们利用非凸的Schatten p - 泛函进行秩逼近，而不是核范数。
仿真结果表明，所提出的方法比先前的最先进算法表现更好。
