TY - GEN
T1 - Non-stationary generalized wishart processes for enhancing resolution over diffusion tensor fields
AU - Cuellar-Fierro, Jhon F.
AU - Vargas-Cardona, Hernán Darío
AU - Álvarez, Andrés M.
AU - Orozco, Álvaro A.
AU - Álvarez, Mauricio A.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057175039&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03801-4_33
DO - 10.1007/978-3-030-03801-4_33
M3 - Conference contribution
AN - SCOPUS:85057175039
SN - 9783030038007
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 371
EP - 381
BT - Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings
A2 - Xu, Kai
A2 - Lin, Stephen
A2 - Boyle, Richard
A2 - Alsallakh, Bilal
A2 - Turek, Matt
A2 - Ramalingam, Srikumar
A2 - Bebis, George
A2 - Parvin, Bahram
A2 - Yang, Jing
A2 - Ventura, Jonathan
A2 - Koracin, Darko
A2 - Cuervo, Eduardo
PB - Springer Verlag
T2 - 13th International Symposium on Visual Computing, ISVC 2018
Y2 - 19 November 2018 through 21 November 2018
ER -