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Principal component analysis with pre-normalization improves the signal-to-noise ratio and image quality in positron emission tomography studies of amyloid deposits in Alzheimer's disease
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 University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Oncology, Radiology and Clinical Immunology.
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2009 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 54, no 11, 3595-3612 p.Article in journal (Refereed) Published
Abstract [en]

This study introduces a new approach for the application of principal component analysis (PCA) with pre-normalization on dynamic positron emission tomography (PET) images. These images are generated using the amyloid imaging agent N-methyl [C-11]2-(4'-methylaminophenyl)-6-hydroxybenzothiazole ([C-11]PIB) in patients with Alzheimer's disease (AD) and healthy volunteers (HVs). The aim was to introduce a method which, by using the whole dataset and without assuming a specific kinetic model, could generate images with improved signal-to-noise and detect, extract and illustrate changes in kinetic behavior between different regions in the brain. Eight AD patients and eight HVs from a previously published study with [C-11] PIB were used. The approach includes enhancement of brain regions where the kinetics of the radiotracer are different from what is seen in the reference region, pre-normalization for differences in noise levels and removal of negative values. This is followed by slice-wise application of PCA (SW-PCA) on the dynamic PET images. Results obtained using the new approach were compared with results obtained using reference Patlak and summed images. The new approach generated images with good quality in which cortical brain regions in AD patients showed high uptake, compared to cerebellum and white matter. Cortical structures in HVs showed low uptake as expected and in good agreement with data generated using kinetic modeling. The introduced approach generated images with enhanced contrast and improved signal-to-noise ratio (SNR) and discrimination power (DP) compared to summed images and parametric images. This method is expected to be an important clinical tool in the diagnosis and differential diagnosis of dementia.

Place, publisher, year, edition, pages
2009. Vol. 54, no 11, 3595-3612 p.
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Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-129050DOI: 10.1088/0031-9155/54/11/021ISI: 000266208200021OAI: oai:DiVA.org:uu-129050DiVA: diva2:337504
Available from: 2010-08-06 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Centre for Image AnalysisComputerized Image AnalysisDepartment of Medical SciencesDepartment of Oncology, Radiology and Clinical ImmunologyDepartment of Biochemistry and Organic ChemistryDepartment of Pharmaceutical Biosciences
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Physics in Medicine and Biology
Medical and Health Sciences

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