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 language||English (US)|
|Title of host publication||2012 IEEE Pacific Visualization Symposium|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Feb 2012|
Bibliographical noteKAUST 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.