自适应正则化超分辨率磁共振图像重建

Adaptive regularized super-resolution reconstruction for magnetic resonance images

目的为提高磁共振(MR)图像的分辨率和信噪比,本文提出一种自适应正则化的重建算法,从四幅分别存在亚像素级移动的低分辨MR图像实现超分辨率图像重建;本文提出的新正则化参数使得代价函数在定义域内呈局部凸性,同时在正则化参数中引入图像先验信息,以加强高频细节的恢复。结果表明,新算法能够有效的实现低分辨率MR图像的重建。

Objective To increase the resolution and signal-to-noise ratio（SNR） of magnetic resonance（MR） images,an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice.The new regularization parameter,which allowed the cost function of the new algorithm to be locally convex within the definition region,was introduced by the piori information to enhance detail restoration of the image with a high frequency.The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.