Non-local DWI Image Super-resolution with Joint Information Based on GPU Implementation

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.

• Publication year

  2019

• Venue

  Computers Materials & Continua

• Publication date

  Unknown publication date

• Fields of study

  Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.32604/cmc.2019.06029
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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