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Deriving and Visualizing Uncertainty in Kinetic PET Modeling
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization Group)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization Group)ORCID iD: 0000-0002-2849-6146
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization Group)ORCID iD: 0000-0002-9466-9826
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization Group)
2012 (English)In: Eurographics Workshop on Visual Computing for Biology and Medicine, 2012 / [ed] T. Ropinski, A. Ynnerman, C. Botha, and J. Roerdink, The Eurographics Association , 2012, 107-114 p.Conference paper, Published paper (Refereed)
Abstract [en]

Kinetic 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.

Place, publisher, year, edition, pages
The Eurographics Association , 2012. 107-114 p.
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778 (print) 2070-5786 (online) ; 2012
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-92834DOI: 10.2312/VCBM/VCBM12/107-114ISBN: 978-3-905674-38-5 (print)OAI: oai:DiVA.org:liu-92834DiVA: diva2:623034
Conference
The third Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2012), 27-28 September 2012, Norrköping, Sweden
Available from: 2013-05-24 Created: 2013-05-24 Last updated: 2017-03-17
In thesis
1. Supporting Quantitative Visual Analysis in Medicine and Biology in the Presence of Data Uncertainty
Open this publication in new window or tab >>Supporting Quantitative Visual Analysis in Medicine and Biology in the Presence of Data Uncertainty
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The advents of technologies have led to tremendous increases in the diversity and size of the available data. In the field of medicine, the advancements in medical imaging technologies have dramatically improved the quality of the acquired data, such as a higher resolution and higher signal-to-noise ratio. In addition, the dramatic reduction of the acquisition time has enabled the studies of organs under function.At the same pace, the progresses in the field of biology and bioinformatics have led to stable automatic algorithms for the generation of biological data. As the amount of the available data and the complexity increase, there have been great demands on efficient analysis and visualization techniques to support quantitative visual analysis of the huge amount of data that we are facing.

This thesis aims at supporting quantitative visual analysis in the presence of data uncertainty within the context of medicine and biology. In this thesis, we present several novel analysis techniques and visual representations to achieve these goals. The results presented in this thesis cover a wide range of applications, which reflects the interdisciplinary nature of scientific visualization, as visualization is not for the sake of visualization. The advances in visualization enable the advances in other fields.

In typical clinical applications or research scenarios, it is common to have data from different modalities. By combining the information from these data sources, we can achieve better quantitative analysis as well as visualization. Nevertheless, there are many challenges involved along the process such as the co-registration, differences in resolution, and signal-to-noise ratio. We propose a novel approach that uses light as an information transporter to address the challenges involved when dealing with multimodal data.

When dealing with dynamic data, it is essential to identify features of interest across the time steps to support quantitative analyses. However, this is a time-consuming process and is prone to inconsistencies and errors. To address this issue, we propose a novel technique that enables an automatic tracking of identified features of interest across time steps in dynamic datasets.

Although technological advances improve the accuracy of the acquired data, there are other sources of uncertainty that need to be taken into account. In this thesis, we propose a novel approach to fuse the derived uncertainty from different sophisticated algorithms in order to achieve a new set of outputs with a lower level of uncertainty. In addition, we also propose a novel visual representation that not only supports comparative visualization, but also conveys the uncertainty in the parameters of a complex system.

Over past years, we have witnessed the rapid growth of available data in the field of biology. The sequence alignments of the top 20 protein domains and families have a large number of sequences, ranging from more than 70,000 to approximately 400,000 sequences. Consequently, it is difficult to convey features using the traditional representation. In this thesis, we propose a novel representation that facilitates the identification of gross trend patterns and variations in large-scale sequence alignment data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 146 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1569
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-103799 (URN)10.3384/diss.diva-103799 (DOI)978-91-7519-415-8 (ISBN)
Public defence
2014-03-07, Dome, Visualization Center, Kungsgatan 54, Norrköping, 10:00 (English)
Opponent
Supervisors
Note

The ISBN 978-91-7519-514-8 on the title page is incorrect. The correct ISBN is 978-91-7519-415-8.

Available from: 2014-01-28 Created: 2014-01-27 Last updated: 2015-09-22Bibliographically approved

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uncertaintyKineticPET(1149 kB)334 downloads
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Nguyen, KhoaBock, AlexanderYnnerman, AndersRopinski, Timo

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