Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Zheng, Hong; Qu, Xiaobo; Bai, Zhengjian; Liu, Yunsong; Guo, Di; Dong, Jiyang; Peng, Xi; Chen, Zhong
• Is Part Of: BMC medical imaging. Volume 17:Issue 1(2017)
• Page Start: 1
• Page End: 13
• Publication Date: 2017-12
• Subjects: Super-resolution -- Multi-contrast -- Statistical information -- Weight -- Non-iterative process; Diagnostic imaging -- Periodicals; 616.075405
• DOI: 10.1186/s12880-016-0176-2
• Languages: English
• ISSNs: 1471-2342

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusion 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. Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights