Non-stationary Generalized Wishart Processes for Enhancing Resolution over Diffusion Tensor Fields View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-10

AUTHORS

Jhon F. Cuellar-Fierro , Hernán Darío Vargas-Cardona , Andrés M. Álvarez , Álvaro A. Orozco , Mauricio A. Álvarez

ABSTRACT

Low spatial resolution of diffusion resonance magnetic imaging (dMRI) restricts its clinical applications. Usually, the measures are obtained in a range from 1 to 2 per voxel, and some structures cannot be studied in detail. Due to clinical acquisition protocols (exposure time, field strength, among others) and technological limitations, it is not possible to acquire images with high resolution. In this work, we present a methodology for enhancing the spatial resolution of diffusion tensor (DT) fields obtained from dMRI. The proposed methodology assumes that a DT field follows a generalized Wishart process (GWP), which is a stochastic process defined over symmetric and positive definite matrices indexed by spatial coordinates. A GWP is modulated by a set of Gaussian processes (GPs). Therefore, the kernel hyperparameters of the GPs control the spatial dynamic of a GWP. Following this notion, we employ a non-stationary kernel for describing DT fields whose statistical properties are not constant over the space. We test our proposed method in synthetic and real dMRI data. Results show that non-stationary GWP can describe complex DT fields (i.e. crossing fibers where the shape, size and orientation properties change abruptly), and it is a competitive methodology for interpolation of DT fields, when we compare with methods established in literature evaluating Frobenius and Riemann distances. More... »

PAGES

371-381

References to SciGraph publications

Book

TITLE

Advances in Visual Computing

ISBN

978-3-030-03800-7
978-3-030-03801-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-03801-4_33

DOI

http://dx.doi.org/10.1007/978-3-030-03801-4_33

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1109840781


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