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Proceedings Paper

Bayesian super-resolution in brain diffusion weighted magnetic resonance imaging (DW-MRI)
Author(s): Juan S. Celis A.; Nelson F. Velasco T.; Julio E. Villalon-Reina; Paul M. Thompson; Eduardo Romero C.
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Paper Abstract

In this paper, a Bayesian super resolution (SR) method obtains high resolution (HR) brain Diffusion-Weighted Magnetic Resonance Imaging (DMRI) images from degraded low resolution (LR) images. Under a Bayesian formulation, the unknown HR image, the acquisition process and the unknown parameters are modeled as stochastic processes. The likelihood model is modeled using a Gaussian distribution to estimate the error between the a linear representation and the observations. The prior is introduced as a Multivariate Gaussian Distribution, for which the inverse of the covariance matrix is approximated by Laplacian-like functions that model the local relationships, capturing thereby non-homogeneous relationships between neighbor intensities. Experimental results show the method outperforms the base line by 2.56 dB when using PSNR as a metric of quality in a set of 35 cases.

Paper Details

Date Published: 26 January 2017
PDF: 7 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101601J (26 January 2017); doi: 10.1117/12.2256918
Show Author Affiliations
Juan S. Celis A., Univ. Nacional de Colombia (Colombia)
Nelson F. Velasco T., Univ. Nacional de Colombia (Colombia)
Univ. Militar Nueva Granada (Colombia)
Julio E. Villalon-Reina, The Univ. of Southern California (United States)
Paul M. Thompson, The Univ. of Southern California (United States)
Eduardo Romero C., Univ. Nacional de Colombia (Colombia)

Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

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