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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 6462832, 12 pages
https://doi.org/10.1155/2017/6462832
Research Article

Second-Order Regression-Based MR Image Upsampling

Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China

Correspondence should be addressed to Jiliu Zhou; nc.ude.tiuc@uilijuohz

Received 4 January 2017; Revised 9 March 2017; Accepted 15 March 2017; Published 30 March 2017

Academic Editor: Giancarlo Ferrigno

Copyright © 2017 Jing Hu 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.

Abstract

The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts.