Non-stationary generalized wishart processes for enhancing resolution over diffusion tensor fields

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

Producción: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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 mm3 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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings
EditoresKai Xu, Stephen Lin, Richard Boyle, Bilal Alsallakh, Matt Turek, Srikumar Ramalingam, George Bebis, Bahram Parvin, Jing Yang, Jonathan Ventura, Darko Koracin, Eduardo Cuervo
EditorialSpringer Verlag
Número de páginas11
ISBN (versión impresa)9783030038007
EstadoPublicada - 2018
Publicado de forma externa
Evento13th International Symposium on Visual Computing, ISVC 2018 - Las Vegas, NV, Estados Unidos
Duración: 19 nov. 201821 nov. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11241 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia13th International Symposium on Visual Computing, ISVC 2018
País/TerritorioEstados Unidos
CiudadLas Vegas, NV


Profundice en los temas de investigación de 'Non-stationary generalized wishart processes for enhancing resolution over diffusion tensor fields'. En conjunto forman una huella única.

Citar esto