# Probabilistic Diffusion MRI Fiber Tracking Using a Directed Acyclic Graph Auto-Regressive Model of Positive Definite Matrices

## TL;DR

This paper introduces a probabilistic fiber tracking method for diffusion MRI using a directed acyclic graph auto-regressive model of positive definite matrices, enhancing uncertainty quantification in tissue connection mapping.

## Contribution

It proposes a novel directed acyclic graph auto-regressive model for positive definite matrices and applies it to probabilistic fiber tracking in diffusion MRI, addressing uncertainty quantification.

## Key findings

- Demonstrates effectiveness through real data analysis.
- Shows improved uncertainty quantification in fiber tracking.
- Validates approach with numerical studies.

## Abstract

Diffusion MRI is a neuroimaging technique measuring the anatomical structure of tissues. Using diffusion MRI to construct the connections of tissues, known as fiber tracking, is one of the most important uses of diffusion MRI. Many techniques are available recently but few properly quantify statistical uncertainties. In this paper, we propose a directed acyclic graph auto-regressive model of positive definite matrices and apply a probabilistic fiber tracking algorithm. We use both real data analysis and numerical studies to demonstrate our proposal.