TY - GEN
T1 - Generalized wishart processes for interpolation over diffusion tensor fields
AU - Cardona, Hernán Darío Vargas
AU - Álvarez, Mauricio A.
AU - Orozco, Álvaro A.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive tool for watching the microstructure of fibrous nerve and muscle tissue. From dMRI, it is possible to estimate 2-rank diffusion tensors imaging (DTI) fields, that are widely used in clinical applications: tissue segmentation, fiber tractography, brain atlas construction, brain conductivity models, among others. Due to hardware limitations of MRI scanners, DTI has the difficult compromise between spatial resolution and signal noise ratio (SNR) during acquisition. For this reason, the data are often acquired with very low resolution. To enhance DTI data resolution, interpolation provides an interesting software solution. The aim of this work is to develop a methodology for DTI interpolation that enhance the spatial resolution of DTI fields. We assume that a DTI field follows a recently introduced stochastic process known as a generalized Wishart process (GWP), which we use as a prior over the diffusion tensor field. For posterior inference, we use Markov Chain Monte Carlo methods. We perform experiments in toy and real data. Results of GWP outperform other methods in the literature, when compared in different validation protocols.
AB - Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive tool for watching the microstructure of fibrous nerve and muscle tissue. From dMRI, it is possible to estimate 2-rank diffusion tensors imaging (DTI) fields, that are widely used in clinical applications: tissue segmentation, fiber tractography, brain atlas construction, brain conductivity models, among others. Due to hardware limitations of MRI scanners, DTI has the difficult compromise between spatial resolution and signal noise ratio (SNR) during acquisition. For this reason, the data are often acquired with very low resolution. To enhance DTI data resolution, interpolation provides an interesting software solution. The aim of this work is to develop a methodology for DTI interpolation that enhance the spatial resolution of DTI fields. We assume that a DTI field follows a recently introduced stochastic process known as a generalized Wishart process (GWP), which we use as a prior over the diffusion tensor field. For posterior inference, we use Markov Chain Monte Carlo methods. We perform experiments in toy and real data. Results of GWP outperform other methods in the literature, when compared in different validation protocols.
UR - http://www.scopus.com/inward/record.url?scp=84952803189&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27863-6_46
DO - 10.1007/978-3-319-27863-6_46
M3 - Conference contribution
AN - SCOPUS:84952803189
SN - 9783319278629
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 508
BT - Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings
A2 - Parvin, Bahram
A2 - Koracin, Darko
A2 - Feris, Rogerio
A2 - Weber, Gunther
A2 - Pavlidis, Ioannis
A2 - McGraw, Tim
A2 - Kopper, Regis
A2 - Ye, Zhao
A2 - Ragan, Eric
A2 - Bebis, George
A2 - Elendt, Mark
A2 - Boyle, Richard
PB - Springer Verlag
T2 - 11th International Symposium on Advances in Visual Computing , ISVC 2015
Y2 - 14 December 2015 through 16 December 2015
ER -