Uncertainty in the DTI Visualization Pipeline

Uncertainty in the DTI Visualization Pipeline teaser image

Diffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as a second order-tensor. The processing pipeline to visualize this data, from image acquisition to the final rendering, is rather complex. It involves a considerable amount of measurements, parameters and model assumptions, all of which generate uncertainties in the final result which typically are not shown to the analyst in the visualization. In recent years, there has been a considerable amount of work on the visualization of uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus on DTI given its simplicity and applicability, however, several aspects presented are valid for DWI as a whole. We explore the various sources of uncertainties involved, approaches for modeling those uncertainties, and, finally, we survey different strategies to visually represent them. We also look at several related methods of uncertainty visualization that have been applied outside DTI and discuss how these techniques can be adopted to the DTI domain. We conclude our discussion with an overview of potential research directions.



Faizan Siddiqui, Thomas Höllt, and Anna Vilanova. Uncertainty in the DTI Visualization Pipeline. In Anisotropy Across Fields and Scales, E. Özarslan, T. Schultz, E. Zhang, A. Fuster (eds), Springer, Chapter 6, pp. 125–148, 2021.


@inbook{ bib:2021_tract_uncertainty_chapter,
author = {Faizan Siddiqui and Thomas H{\"o}llt and Anna Vilanova},
title = { Uncertainty in the DTI Visualization Pipeline },
booktitle = { Anisotropy Across Fields and Scales },
chapter = { 6 },
pages = { 125 -- 148 },
editor = { E. Özarslan, T. Schultz, E. Zhang, A. Fuster },
publisher = { Springer },
year = { 2021 },
doi = { 10.1007/978-3-030-56215-1_6 },