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PCA with Pre-normalization Improves Image Quality in PET 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.
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(English)In: Journal of Nuclear MedicineArticle in journal (Refereed) Submitted
URN: urn:nbn:se:uu:diva-93690OAI: oai:DiVA.org:uu-93690DiVA: diva2:167242
Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2010-03-01Bibliographically approved
In thesis
1. Novel Approaches for Application of Principal Component Analysis on Dynamic PET Images for Improvement of Image Quality and Clinical Diagnosis
Open this publication in new window or tab >>Novel Approaches for Application of Principal Component Analysis on Dynamic PET Images for Improvement of Image Quality and Clinical Diagnosis
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Positron Emission Tomography, PET, can be used for dynamic studies in humans. In such studies a selected part of the body, often the whole brain, is imaged repeatedly after administration of a radiolabelled tracer. Such studies are performed to provide sequences of images reflecting the tracer’s kinetic behaviour, which may be related to physiological, biochemical and functional properties of tissues. This information can be obtained by analyzing the distribution and kinetic behaviour of the administered tracers in different regions, tissues and organs. Each image in the sequence thus contains part of the kinetic information about the administered tracer.

Several factors make analysis of PET images difficult, such as a high noise magnitude and correlation between image elements in conjunction with a high level of non-specific binding to the target and a sometimes small difference in target expression between pathological and healthy regions. It is therefore important to understand how these factors affect the derived quantitative measurements when using different methods such as kinetic modelling and multivariate image analysis.

In this thesis, a new method to explore the properties of the noise in dynamic PET images was introduced and implemented. The method is based on an analysis of the autocorrelation function of the images. This was followed by proposing and implementing three novel approaches for application of Principal Component Analysis, PCA, on dynamic human PET studies. The common underlying idea of these approaches was that the images need to be normalized before application of PCA to ensure that the PCA is signal driven, not noise driven. Different ways to estimate and correct for the noise variance were investigated. Normalizations were carried out Slice-Wise (SW), for the whole volume at once, and in both image domain and sinogram domain respectively. We also investigated the value of masking out and removing the area outside the brain for the analysis.

The results were very encouraging. We could demonstrate that for phantoms as well as for real image data, the applied normalizations allow PCA to reveal the signal much more clearly than what can be seen in the original image data sets. Using our normalizations, PCA can thus be used as a multivariate analysis technique that without any modelling assumptions can separate important kinetic information into different component images. Furthermore, these images contained optimized signal to noise ratio (SNR), low levels of noise and thus showed improved quality and contrast. This should allow more accurate visualization and better precision in the discrimination between pathological and healthy regions. Hopefully this can in turn lead to improved clinical diagnosis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. x + 89 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 114
Bildanalys, Dynamic PET, Noise Correlation, PCA, Image Quality, Slice-Wise, Pixel-Wise, Masked-Volume-Wise, Pre-normalization, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:uu:diva-6053 (URN)91-554-6387-8 (ISBN)
Public defence
2005-12-02, Room IX, Universitetshuset, Sankt Olofsgatan 10B, Uppsala, 10:15
Available from: 2005-11-11 Created: 2005-11-11Bibliographically approved

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