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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 3647202, 9 pages
Research Article

Diffusion-Weighted Images Superresolution Using High-Order SVD

1Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
2College of Electronic Engineering, Sichuan University, Chengdu 610065, China
3Department of Computer Science, Xihua University, Chengdu 610039, China

Received 27 January 2016; Revised 9 July 2016; Accepted 28 July 2016

Academic Editor: Po-Hsiang Tsui

Copyright © 2016 Xi Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.