Metrics for uncertainty analysis and visualization of diffusion tensor images

Authors:
Fangxiang Jiao;Jeff M. Phillips;Jeroen Stinstra;Jens Krüger;Raj Varma;Edward Hsu;Julie Korenberg;Chris R. Johnson
Affiliations:
The Scientific Computing and Imaging Institute, University of Utah;The School of Computing, University of Utah;Numira Biosciences;DFKI, MMCI, Saarbrcken;The School of Computing, University of Utah;The Department of Biomedical Engineering, the University of Utah;The Brain Institute, Department of Pediatrics, University of Utah;The Scientific Computing and Imaging Institute, University of Utah
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

Citing 5
Cited 1

Tensorlines: advection-diffusion based propagation through diffusion tensor fields

VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Top Scientific Visualization Research Problems

IEEE Computer Graphics and Applications
Parameter Sensitivity Visualization for DTI Fiber Tracking

IEEE Transactions on Visualization and Computer Graphics
Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Surface matching via currents

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging

HiFiVE: a hilbert space embedding of fiber variability estimates for uncertainty modeling and visualization

EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose three metrics to quantify the differences between the results of diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracking algorithms: the area between corresponding fibers of each bundle, the Earth Mover's Distance (EMD) between two fiber bundle volumes, and the current distance between two fiber bundle volumes. We also discuss an interactive fiber track comparison visualization toolkit we have developed based on the three proposed fiber difference metrics and have tested on six widely-used fiber tracking algorithms. To show the effectiveness and robustness of our metrics and visualization toolkit, we present results on both synthetic data and high resolution monkey brain DT-MRI data. Our toolkit can be used for testing the noise effects on fiber tracking analysis and visualization and to quantify the difference between any pair of DT-MRI techniques, compare single subjects within an image atlas.