Super-resolution reconstruction of diffusion-weighted images using 4D low-rank and total variation

Feng Shi, Jian Cheng, Li Wang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input lowresolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is four-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.

Original languageEnglish
Title of host publicationMathematics and Visualization
PublisherSpringer Heidelberg
Pages15-25
Number of pages11
Volumenone
ISBN (Print)9783319285863
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameMathematics and Visualization
Volumenone
ISSN (Print)16123786
ISSN (Electronic)2197666X

Other

OtherWorkshop on Computational Diffusion MRI, MICCAI 2015
CountryGermany
CityMunich
Period15/10/915/10/9

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Geometry and Topology
  • Modelling and Simulation

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  • Cite this

    Shi, F., Cheng, J., Wang, L., Yap, P. T., & Shen, D. (2016). Super-resolution reconstruction of diffusion-weighted images using 4D low-rank and total variation. In Mathematics and Visualization (Vol. none, pp. 15-25). (Mathematics and Visualization; Vol. none). Springer Heidelberg. https://doi.org/10.1007/978-3-319-28588-7_2