自适应正则化超分辨率MR图像重建(英文)

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

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。

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 derivative 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.