Interactive formation of statistical hypotheses in diffusion tensor imaging
Abbasloo, A. ; Wiens, V. ; Schmidt-Wilcke, T. ; Sundgren, P. LU
; Klein, R.
and Schultz, T.
(2019)
2019 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019
In Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019
p.33-43
- 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... (More)
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.
(Less)
- author
- Abbasloo, A.
; Wiens, V.
; Schmidt-Wilcke, T.
; Sundgren, P.
LU
; Klein, R.
and Schultz, T.
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019
- series title
- Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019
- editor
- Kozlikova, Barbora and Raidou, Renata Georgia
- pages
- 11 pages
- publisher
- Eurographics - European Association for Computer Graphics
- conference name
- 2019 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019
- conference location
- Brno, Czech Republic
- conference dates
- 2019-09-04 - 2019-09-06
- external identifiers
-
- scopus:85087441110
- ISBN
- 9783038680819
- DOI
- 10.2312/vcbm.20191229
- language
- English
- LU publication?
- yes
- id
- 055d2824-c47e-4937-a918-00ae9a3f4643
- date added to LUP
- 2020-07-20 13:06:50
- date last changed
- 2025-04-04 15:08:13
@inproceedings{055d2824-c47e-4937-a918-00ae9a3f4643,
abstract = {{<p>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.</p>}},
author = {{Abbasloo, A. and Wiens, V. and Schmidt-Wilcke, T. and Sundgren, P. and Klein, R. and Schultz, T.}},
booktitle = {{Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019}},
editor = {{Kozlikova, Barbora and Raidou, Renata Georgia}},
isbn = {{9783038680819}},
language = {{eng}},
pages = {{33--43}},
publisher = {{Eurographics - European Association for Computer Graphics}},
series = {{Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019}},
title = {{Interactive formation of statistical hypotheses in diffusion tensor imaging}},
url = {{http://dx.doi.org/10.2312/vcbm.20191229}},
doi = {{10.2312/vcbm.20191229}},
year = {{2019}},
}