Interactive Formation of Statistical Hypotheses in Diffusion Tensor Imaging

Amin Abbasloo, Vitalis Wiens, Tobias Schmidt-Wilcke, Pia C. Sundgren, Reinhard Klein, and Thomas Schultz
In proceedings of EG Workshop on Visual Computing for Biology and Medicine, 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.

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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},
      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.}
}