title: Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity.
abstract: Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution.
  
  
  
  In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart.
  
  
  
  The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments.
  
  
  
  Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights.
authors.affiliation: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.|School of Computer Science and Engineering, Key Laboratory of Intelligent Processing of Image and Graphics, Guilin University of Electronic Technology, Guilin, 541004, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China. quxiaobo@xmu.edu.cn.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Qu
authors.affiliation: School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China.
authors.forename: Zhengjian
authors.initials: Z
authors.lastname: Bai
authors.affiliation: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
authors.forename: Yunsong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen, 361024, China.
authors.forename: Di
authors.initials: D
authors.lastname: Guo
authors.affiliation: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
authors.forename: Jiyang
authors.initials: J
authors.lastname: Dong
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Peng
authors.affiliation: Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China. chenz@xmu.edu.cn.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D003287:Contrast Media / Q000378:metabolism; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*
chemical_list: D003287:Contrast Media
keywords: Multi-contrast; Non-iterative process; Statistical information; Super-resolution; Weight