2002 Fiscal Year Final Research Report Summary
High speed algorithm for statistically reliable parametric image in positron emission tomography without continuous arterial blood sampling
| Project/Area Number |
13670977
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Radiation science
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| Research Institution | Tokyo Metropolitan Institute of Gerontology |
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Principal Investigator |
KIMURA Yuichi Senior Research Scientist, Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, ポジトロン医学研究部門, 主任研究員 (60205002)
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| Co-Investigator(Kenkyū-buntansha) |
ODA Keiichi Research Assistant, Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, ポジトロン医学研究部門, 研究助手 (70224235)
ISHII Kenji Research Assistant, Positron Medical Center, Tokyo Metropolitan Institute of Gerontology, ポジトロン医学研究部門, 研究助手 (10231135)
MATANI Ayumu Associate Professor, Department of Mathematical Engineering and Information Physics, The University Tokyo, 大学院・新領域創成科学研究科, 助教授 (50273842)
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| Project Period (FY) |
2001 – 2002
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| Keywords | PET / arterial blood sampling / compartment model / kinetic analysis / clustering / independent component analysis / mixture Gaussian model / nuclear medicine |
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| Research Abstract |
Voxel-based kinetic analysis in PET can visualize various functionalities in a living tissue. Major drawbacks in formation of an parametic image are bad noise statistics in voxel-based PET data. A large amount of voxels in PET causes a huge calculation time, and it makes formation of a parametric image inpractical. The statistical model-based clustering algorithm of CAKS (Clustering Analysis for Kinetics) is proposed to categorize voxels whose PET data has a similar shape and has similar functionalities. In CAKS, PET data was projected in a feature space and mixture Gaussian model was ulitized. As results, a parametric image can be formed in 30 minutes and the estimates were correspond to the estimates by an ordinal ROI-based estimation. Arterial blood sampling is other problem to apply CAKS in a clinical situation. The spatial distribution of tissue and blood vessel in a brain is distinguishable, and statistical algorithm for a blind separation of independent component analysis was applicable. After specialy designed data preprocessing schemes to emphasize the difference between spatial distribution of vessel and tissue, a proposed algorithm can estimate time course of activity in arterial blood and it corresponded to a measured time activity curve.
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