26 January 2017 Bayesian super-resolution in brain diffusion weighted magnetic resonance imaging (DW-MRI)
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Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101601J (2017) https://doi.org/10.1117/12.2256918
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina
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.
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Juan S. Celis A., Nelson F. Velasco T., Julio E. Villalon-Reina, Paul M. Thompson, Eduardo Romero C., "Bayesian super-resolution in brain diffusion weighted magnetic resonance imaging (DW-MRI)", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101601J (26 January 2017); doi: 10.1117/12.2256918; https://doi.org/10.1117/12.2256918
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