基于压缩感知的自适应正则化磁共振图像重构

Compressed sensing-adaptive regularization for reconstruction of magnetic resonance image

当前基于压缩传感理论的正则化磁共振(CS-MR)图像重构算法普遍采用全局正则化参数,不能很好地在保持边缘和平滑噪声方面做出平衡。为此,提出一种自适应的正则化CS-MRI重构算法。结合图像稀疏性和其局部光滑性的先验知识,采用非线性共轭梯度下降算法求取最优化问题,并在迭代过程中自适应地改变局部正则化参数。新的正则化参数可以更好地恢复图像边缘,并且有利于平滑噪声,使代价函数在定义域内具有凸性;同时先验信息包含于正则化参数中,以提高图像的高频成分。实验结果表明该算法能有效权衡恢复图像边缘和平滑噪声两者的关系。

The current Magnetic Resonance （MR） image reconstruction algorithms based on compressed sensing （ CS-MR） commonly use global regularization parameter, which results in the inferior reconstruction that cannot restore the image edges and smooth the noise at the same time. In order to solve this problem, based on adaptive regularization and compressed sensing, the reconstruction method that used the sparse priors and the local smooth priors of MR image in combination was proposed. Nonlinear conjugate gradient method was used for solving the optimized procedure, and the local regularization parameter was adaptively changed during the iterative process. The regularization parameter can recover the image＇s edge and simultaneously smooth the noise, making cost function convex within the definition region. The prior information is involved in the regularization parameter to improve the high frequency components of the image. Finally, the experimental results show that the proposed method can effectively restore the image edges and smooth the noise.