  Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive 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.
