Comparative and ensemble visualization of diffusion tensor fields

Doctoral thesis (2017)

Authors

C. Zhang Computer Graphics and Visualisation - EEMCS
Research Group
Computer Graphics and Visualisation (EEMCS) (TU Delft)
Copyright: © 2017 C. Zhang
DOI: https://doi.org/10.4233/uuid:5b0da969-6e43-4cfa-bd33-09494ecfe199
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Published Date

2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

Abstract

Scientific visualization of tensor fields is challenging due to the complex and multivariate nature of tensor data. The visualization of multiple tensor fields becomes even more difficult, and still in its infancy. This thesis aims at contributing visual analysis techniques for multiple 3D tensor fields.
We focus specifically on the visual analysis of Diffusion Tensor Imaging (DTI)
datasets. DTI is a magnetic resonance imaging (MRI) based modality, which is commonly used in neuroscience to investigate brain white matter in vivo. It requires a long scanning time compared to other imaging modalities. Acceleration of MRI acquisitions has the potential to improve the applicability of DTI. Compressed sensing (CS) is a signal reconstruction technique that is used to accelerate MRI acquisitions. The traditional CS method aims at optimizing the global quality of the reconstructed image.
However, in practice, the quality of local structures is often of more interest. Therefore, we investigate CS for this purpose and contribute in this direction by adapting the traditional CS reconstruction method to focus on the quality of local structures.

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