
Proceedings Paper
Sparse dictionary representation and propagation for MRI volume super-resolutionFormat | Member Price | Non-Member Price |
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Paper Abstract
This study addresses the problem of generating a high-resolution (HR) MRI volume from a single low-resolution (LR)
MRI input volume. Recent researches have proved that sparse coding can be successfully applied for single-frame super-resolution for natural images, which is based on good reconstruction of any local image patch with a sparse linear combination of atoms taken from an appropriate over-complete dictionary. This study adapts the basic idea of sparse code-based
super-resolution (SCSR) for MRI volume data, and then improves the dictionary learning strategy in the conventional
SCSR for achieving the precise sparse representation of HR volume patches. In the proposed MRI super-resolution strategy, we only learn the dictionary of the HR MRI volume patches with sparse coding algorithm, and then propagate the HR
dictionary to the LR dictionary by mathematical analysis for calculating the sparse representation (coefficients) of any LR
local input volume patch. The unknown corresponding HR volume patch can be reconstructed with the sparse coefficients
from the LR volume patch and the corresponding HR dictionary. We validate that the proposed SCSR strategy through
dictionary propagation can recover much clearer and more accurate HR MRI volume than the conventional interpolated
methods.
Paper Details
Date Published: 13 March 2013
PDF: 11 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692N (13 March 2013); doi: 10.1117/12.2008145
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 11 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692N (13 March 2013); doi: 10.1117/12.2008145
Show Author Affiliations
Xian-Hua Han, Ritsumeikan Univ. (Japan)
Yen-Wei Chen, Ritsumeikan Univ. (Japan)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
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