Uncertainty visualization in HARDI based on ensembles of ODFs

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

doi:10.1109/PacificVis.2012.6183591

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 proceeding › Conference contribution

33 Scopus citations

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.

Original language	English (US)
Title of host publication	2012 IEEE Pacific Visualization Symposium
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	193-200
Number of pages	8
ISBN (Print)	9781467308663
DOIs	https://doi.org/10.1109/PacificVis.2012.6183591
State	Published - Feb 2012
Externally published	Yes

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.

Access to Document

10.1109/PacificVis.2012.6183591

Cite this

@inproceedings{b4a7156a7be4497eba480a041442458f,

title = "Uncertainty visualization in HARDI based on ensembles of ODFs",

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. {\textcopyright} 2012 IEEE.",

author = "Fangxiang Jiao and Phillips, {Jeff M.} and Yaniv Gur and Johnson, {Chris R.}",

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.",

year = "2012",

month = feb,

doi = "10.1109/PacificVis.2012.6183591",

language = "English (US)",

isbn = "9781467308663",

pages = "193--200",

booktitle = "2012 IEEE Pacific Visualization Symposium",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

TY - GEN

T1 - Uncertainty visualization in HARDI based on ensembles of ODFs

AU - Jiao, Fangxiang

AU - Phillips, Jeff M.

AU - Gur, Yaniv

AU - Johnson, Chris R.

N1 - 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.

PY - 2012/2

Y1 - 2012/2

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/600126

UR - http://ieeexplore.ieee.org/document/6183591/

UR - http://www.scopus.com/inward/record.url?scp=84860663605&partnerID=8YFLogxK

U2 - 10.1109/PacificVis.2012.6183591

DO - 10.1109/PacificVis.2012.6183591

M3 - Conference contribution

C2 - 24466504

SN - 9781467308663

SP - 193

EP - 200

BT - 2012 IEEE Pacific Visualization Symposium

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Uncertainty visualization in HARDI based on ensembles of ODFs

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this