title: Principal component analysis of dynamic positron emission tomography images.
abstract: Multivariate image analysis can be used to analyse multivariate medical images. The purpose could be to visualize or classify structures in the image. One common multivariate image analysis technique which can be used for visualization purposes is principal component analysis (PCA). The present work concerns visualization of organs and structures with different kinetics in a dynamic sequence utilizing PCA. When applying PCA on positron emission tomography (PET) images, the result is initially not satisfactory. It is illustrated that one major explanation for the behaviour of PCA when applied to PET images is that it is a data-driven technique which cannot separate signals from high noise levels. With a better understanding of the PCA, gained with a strategy of examining the image data set, the transformations, and the results using visualization tools, a surprisingly easily understood methodology can be derived. The proposed methodology can enhance clinically interesting information in a dynamic PET imaging sequence in the first few principal component images and thus should be able to aid in the identification of structures for further analysis.
authors.affiliation: Centre for Image Analysis, Uppsala University, Sweden.
authors.forename: F
authors.initials: F
authors.lastname: Pedersen
authors.forename: M
authors.initials: M
authors.lastname: Bergström
authors.forename: E
authors.initials: E
authors.lastname: Bengtsson
authors.forename: B
authors.initials: B
authors.lastname: Långström
mesh_terms: D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging; D015999:Multivariate Analysis; D010183:Pancreatic Ducts / Q000000981:diagnostic imaging; D012815:Signal Processing, Computer-Assisted; D014055:Tomography, Emission-Computed / Q000379:methods*