Interactive Formation of Statistical Hypotheses in Diffusion Tensor Imaging
Abstract
When Diffusion Tensor Imaging (DTI) is used in clinical studies, statistical hypothesis testing is the standard approach to establish significant differences between groups, such as patients and healthy controls. However, diffusion tensors contain six degrees of freedom, and the most commonly used univariate tests reduce them to a single scalar, such as Fractional Anisotropy. Multivariate tests that account for the full tensor information have been developed, but have not been widely adopted in practice. Based on analyzing the limitations of existing univariate and multivariate tests, we argue that it is beneficial to use a more flexible, steerable test. Therefore, we introduce a test that can be customized to include any subset of tensor attributes that are relevant to the analysis task at hand. We also present a visual analytics system that supports the exploratory task of customizing it to a specific scenario. Our system closely integrates quantitative analysis with suitable visualizations. It links spatial and abstract views to reveal clusters of strong differences, to relate them to the affected anatomical structures, and to visually compare the results of different tests. A use case is presented in which our system leads to the formation of several new hypotheses about the effects of systemic lupus erythematosus on water diffusion in the brain.
Official version at https://dx.doi.org/10.2312/vcbm.20191229
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Bibtex
@INPROCEEDINGS{Abbasloo:VCBM2019,
author = {Abbasloo, Amin and Wiens, Vitalis and Schmidt-Wilcke, Tobias and Sundgren, Pia C. and Klein,
Reinhard and Schultz, Thomas},
pages = {33--43},
title = {Interactive Formation of Statistical Hypotheses in Diffusion Tensor Imaging},
booktitle = {EG Workshop on Visual Computing for Biology and Medicine},
year = {2019},
abstract = {When Diffusion Tensor Imaging (DTI) is used in clinical studies, statistical hypothesis testing is
the standard approach to establish significant differences between groups, such as patients and
healthy controls. However, diffusion tensors contain six degrees of freedom, and the most commonly
used univariate tests reduce them to a single scalar, such as Fractional Anisotropy. Multivariate
tests that account for the full tensor information have been developed, but have not been widely
adopted in practice. Based on analyzing the limitations of existing univariate and multivariate
tests, we argue that it is beneficial to use a more flexible, steerable test. Therefore, we
introduce a test that can be customized to include any subset of tensor attributes that are relevant
to the analysis task at hand. We also present a visual analytics system that supports the
exploratory task of customizing it to a specific scenario. Our system closely integrates
quantitative analysis with suitable visualizations. It links spatial and abstract views to reveal
clusters of strong differences, to relate them to the affected anatomical structures, and to
visually compare the results of different tests. A use case is presented in which our system leads
to the formation of several new hypotheses about the effects of systemic lupus erythematosus on
water diffusion in the brain.},
doi = {10.2312/vcbm.20191229}
}