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    Uncertainty visualization in HARDI based on ensembles of ODFs

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    Type
    Conference Paper
    Authors
    Jiao, Fangxiang
    Phillips, Jeff M.
    Gur, Yaniv
    Johnson, Chris R.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2012-02
    Permanent link to this record
    http://hdl.handle.net/10754/600126
    
    Metadata
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    Abstract
    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.
    Citation
    Jiao F, Phillips JM, Gur Y, Johnson CR (2012) Uncertainty visualization in HARDI based on ensembles of ODFs. 2012 IEEE Pacific Visualization Symposium. Available: http://dx.doi.org/10.1109/PacificVis.2012.6183591.
    Sponsors
    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).
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2012 IEEE Pacific Visualization Symposium
    DOI
    10.1109/PacificVis.2012.6183591
    PubMed ID
    24466504
    PubMed Central ID
    PMC3898522
    ae974a485f413a2113503eed53cd6c53
    10.1109/PacificVis.2012.6183591
    Scopus Count
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