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Signal Extraction and Separation in In Vivo Animal PET Studies with Masked Volumewise Principal-Component Analysis
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences. (Endokrin tumörbiologi)
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2010 (English)In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 38, no 2, 53-60 p.Article in journal (Refereed) Published
Abstract [en]

The standardized uptake value is commonly used as a tool tosupplement visual interpretation and to quantify the imagesacquired from static in vivo animal PET. The preferred approachfor analyzing PET data is either to sum the images and calculatethe standardized uptake value or to use kinetic modeling. Theaim of this study was to investigate the performance of maskedvolumewise principal-component analysis (MVW-PCA) used in dynamicin vivo animal PET studies to extract and separate signals withdifferent kinetic behaviors. Methods: PET data were acquiredwith a small-animal PET scanner and a fluorine tracer in a studyof rats and mice. After acquisition, the data were reconstructedby use of 4 time protocols with different frame lengths. Datawere analyzed by use of MVW-PCA with applied noise prenormalizationand a new masking technique developed in this study. Results:The resulting principal-component images showed a clear separationof the activity in the spine into the first MVW-PCA componentand the activity in the kidneys into the second MVW-PCA component.In addition, the different time protocols were shown to havelittle or no impact on the results obtained with MVW-PCA. Conclusion:MVW-PCA can efficiently separate different kinetic behaviorsinto different principal-component images. Moreover, MVW-PCAis a stable technique in the sense that the time protocol chosenhas only a small impact on the resulting principal-componentimages.

Place, publisher, year, edition, pages
2010. Vol. 38, no 2, 53-60 p.
Keyword [en]
masked volumewise principal-component analysis; PET; animal study; dynamic dataset; list mode
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-141027DOI: 10.2967/jnmt.110.075085OAI: oai:DiVA.org:uu-141027DiVA: diva2:384848
Available from: 2011-01-10 Created: 2011-01-10 Last updated: 2016-10-19Bibliographically approved

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