Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling

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dc.contributor.authorPatel, Tarken_US
dc.contributor.authorAthawale, Tushar M.en_US
dc.contributor.authorOuermi, Timbwaoga A. J.en_US
dc.contributor.authorJohnson, Chris R.en_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:38:51Z
dc.date.available2025-05-26T06:38:51Z
dc.date.issued2025
dc.description.abstractIn this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state-of-the-art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state-of-the-art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human-brain HARDI datasets.en_US
dc.description.sectionheadersUncertainty, Sensitivity, Scalability
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70138
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70138
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70138
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Scientific visualization; Computing methodologies → Uncertainty quantification; Ray tracing; Mathematics of computing → Probabilistic algorithms; Bootstrapping
dc.subjectHuman centered computing → Scientific visualization
dc.subjectComputing methodologies → Uncertainty quantification
dc.subjectRay tracing
dc.subjectMathematics of computing → Probabilistic algorithms
dc.subjectBootstrapping
dc.titleFast HARDI Uncertainty Quantification and Visualization with Spherical Samplingen_US
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