@inproceedings{936dbf025ca84ff5b53f5dc5e512cd71,
title = "Bayesian super-resolution in brain diffusion weighted magnetic resonance imaging (DW-MRI)",
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.",
keywords = "Bayesian, Diffusion weighted magnetic resonance imaging, Image processing, Super resolution",
author = "Celis, \{Juan S.A.\} and Velasco, \{Nelson F.T.\} and Villalon-Reina, \{Julio E.\} and Thompson, \{Paul M.\} and Romero, \{Eduardo C.\}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016 ; Conference date: 05-12-2016 Through 07-12-2016",
year = "2017",
doi = "10.1117/12.2256918",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Ignacio Larrabide",
booktitle = "12th International Symposium on Medical Information Processing and Analysis",
address = "United States",
}