自适应正则化超分辨率MR图像重建

背景:超分辨率冲击已经在许多领域展开研究于应用,比如医疗军队,以及视频等.目的:利用自适应正则化超分辨率重建算法,将低梯度场中获得的具有亚象素位移的图像重建出高分辨率、高信噪比的MR图像.方法:采用最小二乘法作为代价函数,并求其导数,以获得迭代公式.在迭代过程中自适应的改变正则化参数和步长.结果与结论:新正则化参数使得代价函数在定义域内具有凸性,同时先验信息被包含于正则化参数中,以提高图像的高频成分.文章提供了低分辨率的体模图像及重建后的MR图像

Self-adaptive regularized super-resolution reconstruction of magnetic resonance images

BACKGROUND: Super-resolution reconstruction has been extensively studied and used in many fields,such as medical diagnostics,military surveillance,frame freeze in video,and remote sensing.OBJECTIVE: In order to obtain high-resolution magnetic resonance images,gradient magnetic field is required and the signal-to-noise will be reduced due to the decrease in voxel size with traditional scan.The present study used a self-adaptive regularized super-resolution reconstruction algorithm to acquire high-resolution magnetic resonance images from four half-pixel-shifted low resolution images.METHODS: The least squares algorithm was used as a cost function.The dedvative of the cost function was calculated to obtain an iterative formula of super-resolution reconstruction.In the process of iterative process,the parameter and step size of image resolution were regularized.RESULTS AND CONCLUSION: The new regularization parameter makes cost function of the new algorithm convex within the definition region.The piori information is involved in the regularization parameter that can improve the high-frequency components of the restored image.As shown from the results obtained in the phantom imaging,the proposed super-resolution technique can improve the resolution of magnetic resonance image.