Diffusion MRI visualization

Review (2018)

Authors

Tim Schultz Bonn-Aachen International Center for Information Technology, Universität Bonn

A. Vilanova Bartroli Computer Graphics and Visualisation -

Research Group

Computer Graphics and Visualisation () (TU Delft)

Diffusion MRI Tractography Visualization Diffusion tensor

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http://resolver.tudelft.nl/uuid:ff95d3eb-0997-4f0f-a203-e5dd3395900e

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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

Abstract

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

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