Uncertainty visualization in HARDI based on ensembles of ODFs

Fangxiang Jiao, Jeff M. Phillips, Yaniv Gur, Chris R. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.
Original languageEnglish (US)
Title of host publication2012 IEEE Pacific Visualization Symposium
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781467308663
StatePublished - Feb 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Supported by NIH/NCRR Center for Integrative Biomedical Computing, 2P41-RR12553-12, Award KUS-C1-016-04, by KAUST, and DOE SciDAC VACET andDOE NETL, by subaward to the Univ. Utah under NSF award 1019343 to CRA, andby NIH Autism Center of Excellence grant (NIMH and NICHD #HD055741).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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