Open Access

ARTICLE

Non-Local DWI Image Super-Resolution with Joint Information Based on GPU Implementation

by Yanfen Guo, Zhe Cui, Zhipeng Yang, Xi Wu, Shaahin Madani

1 Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, Chengdu, 610041, China.
2 Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
3 Department of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, China.
4 Department of Computer Science and IT, RMIT University, 124 La Trobe Street, Melbourne VIC, 3000, Australia.
* Corresponding Author: Zhe Cui. Email: cuizhe@casit.com.cn.

Computers, Materials & Continua 2019, 61(3), 1205-1215. https://doi.org/10.32604/cmc.2019.06029

Abstract

Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran slowly because of the joint information. In order to apply the algorithm to the actual scene, we compare the proposed algorithm on CPU and GPU respectively. It is found that the processing time on GPU is much less than on CPU, and that the highest speedup ratio to the traditional algorithm is more than 26 times. It raises the possibility of applying reconstruction algorithms in actual workplaces.

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Citations