Self-adaptive regularized super-resolution reconstruction of magnetic resonance images / 中国组织工程研究

ABSTRACT

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.