Overview + Detail Visualization for Ensembles of Diffusion Tensors. (4th July 2017)

• Record Type: Journal Article
• Authors: Zhang, C.; Caan, M.W.A.; Höllt, T.; Eisemann, E.; Vilanova, A.
• Is Part Of: Computer graphics forum. Volume 36:Number 3(2017)
• Page Start: 121
• Page End: 132
• Publication Date: 2017-07-04
• Subjects: Categories and Subject Descriptors (according to ACM CCS) -- I.3.5 [Computer Graphics]: —Curve, surface, solid, and object representations -- I.3.8 [Computer Graphics]: Applications—; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13173
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: 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.