Generalized Wishart processes for interpolation over diffusion tensor fields

TL;DR

This paper introduces a novel interpolation method for diffusion tensor imaging (DTI) fields using a generalized Wishart process, improving resolution and accuracy over existing techniques through Bayesian inference.

Contribution

The paper develops a new GWP-based Bayesian framework for DTI interpolation, outperforming previous methods in resolution enhancement and accuracy.

Findings

GWP outperforms existing interpolation methods in validation tests.

Bayesian inference via MCMC effectively estimates the diffusion tensor fields.

The approach improves DTI resolution in both toy and real data experiments.

Abstract

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.