Overview plus Detail Visualization for Ensembles of Diffusion Tensors
| Autor: | Elmar Eisemann, Thomas Höllt, Matthan W.A. Caan, Anna Vilanova, Changgong Zhang |
|---|---|
| Přispěvatelé: | Medical Image Analysis, Visualization, Amsterdam Neuroscience - Brain Imaging, Radiology and Nuclear Medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, ACS - Atherosclerosis & ischemic syndromes |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Scale (ratio)
Computer science I.3.8 [Computer Graphics]: Applications— 02 engineering and technology computer.software_genre and object representations Glyph (data visualization) I.3.5 [Computer Graphics]: —Curve surface solid and object representations 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering surface Tensor Diffusion (business) I.3.5 [Computer Graphics]: —Curve Orientation (computer vision) Aggregate (data warehouse) 020207 software engineering Computer Graphics and Computer-Aided Design Visualization solid Data mining Categories and Subject Descriptors (according to ACM CCS) Algorithm computer Diffusion MRI |
| Zdroj: | Computer Graphics Forum, 36(3), 121-132. Wiley-Blackwell Computer Graphics Forum, 36(3), 121-132 Computer graphics forum, 36(3), 121-132. Wiley-Blackwell |
| ISSN: | 0167-7055 1467-8659 |
| DOI: | 10.1111/cgf.13173 |
| Popis: | A Diffusion Tensor Imaging (DTI) group study consists of a collection of volumetric diffusion tensor datasets (i.e., an ensemble) acquired from a group of subjects. The multivariate nature of the diffusion tensor imposes challenges on the analysis and the visualization. These challenges are commonly tackled by reducing the diffusion tensors to scalar-valued quantities that can be analyzed with common statistical tools. However, reducing tensors to scalars poses the risk of losing intrinsic information about the tensor. Visualization of tensor ensemble data without loss of information is still a largely unsolved problem. In this work, we propose an overview + detail visualization to facilitate the tensor ensemble exploration. We define an ensemble representative tensor and variations in terms of the three intrinsic tensor properties (i.e., scale, shape, and orientation) separately. The ensemble summary information is visually encoded into the newly designed aggregate tensor glyph which, in a spatial layout, functions as the overview. The aggregate tensor glyph guides the analyst to interesting areas that would need further detailed inspection. The detail views reveal the original information that is lost during aggregation. It helps the analyst to further understand the sources of variation and formulate hypotheses. To illustrate the applicability of our prototype, we compare with most relevant previous work through a user study and we present a case study on the analysis of a brain diffusion tensor dataset ensemble from healthy volunteers. |
| Databáze: | OpenAIRE |
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