Deriving and Visualizing Uncertainty in Kinetic PET Modeling

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dc.contributor.authorNguyen, Khoa Tanen_US
dc.contributor.authorBock, Alexanderen_US
dc.contributor.authorYnnerman, Andersen_US
dc.contributor.authorRopinski, Timoen_US
dc.contributor.editorTimo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdinken_US
dc.date.accessioned2013-11-08T10:34:21Z
dc.date.available2013-11-08T10:34:21Z
dc.date.issued2012en_US
dc.description.abstractKinetic modeling is the tool of choice when developing new positron emission tomography (PET) tracers for quantitative functional analysis. Several approaches are widely used to facilitate this process. While all these approaches are inherently different, they are still subject to uncertainty arising from various stages of the modeling process. In this paper we propose a novel approach for deriving and visualizing uncertainty in kinetic PET modeling. We distinguish between intra- and inter-model uncertainties. While intra-model uncertainty allows us to derive uncertainty based on a single modeling approach, inter-model uncertainty arises from the differences of the results of different approaches. To derive intra-model uncertainty we exploit the covariance matrix analysis. The inter-model uncertainty is derived by comparing the outcome of three standard kinetic PET modeling approaches. We derive and visualize this uncertainty to exploit it as a basis for changing model input parameters with the ultimate goal to reduce the modeling uncertainty and thus obtain a more realistic model of the tracer under investigation. To support this uncertainty reduction process, we visually link abstract and spatial data by introducing a novel visualization approach based on the ThemeRiver metaphor, which has been modified to support the uncertainty-aware visualization of parameter changes between spatial locations. We have investigated the benefits of the presented concepts by conducting an evaluation with domain experts.en_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.identifier.isbn978-3-905674-38-5en_US
dc.identifier.issn2070-5778en_US
dc.identifier.urihttps://doi.org/10.2312/VCBM/VCBM12/107-114en_US
dc.publisherThe Eurographics Associationen_US
dc.titleDeriving and Visualizing Uncertainty in Kinetic PET Modelingen_US
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VCBM 12: Eurographics Workshop on Visual Computing for Biology and Medicine