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A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies - Clinical brain studies using [C-11]-GR205171, [C-11]-L-deuterium-deprenyl, [C-11]-5-hydroxy-L-tryptophan, [C-11]-L-DOPA and Pittsburgh Compound-B
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala Imanet.
Uppsala Imanet.
Uppsala Imanet. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
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2006 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 33, no 2, 588-598 p.Article in journal (Refereed) Published
Abstract [en]

Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

Place, publisher, year, edition, pages
2006. Vol. 33, no 2, 588-598 p.
Keyword [en]
positron emission tomography, pre-normalization, principal component analysis, volume-wise, masked volume
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-22880DOI: 10.1016/j.neuroimage.2006.05.060ISI: 000241406800017PubMedID: 16934493OAI: oai:DiVA.org:uu-22880DiVA: diva2:50653
Available from: 2007-01-23 Created: 2007-01-23 Last updated: 2017-12-07Bibliographically approved

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Långström, BengtBengtsson, Ewert

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Centre for Image AnalysisComputerized Image AnalysisDepartment of Pharmaceutical Biosciences
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