Imaging of brain glucose metabolism with 18F-2-fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG PET) can give important information regarding disease-related changes in underlying neuronal systems, when combined with appropriate analytical methods. One such method is the scaled subprofile model combined with principal component analysis (SSM PCA). This model takes into account the relationships (covariance) between voxels to identify disease-related patterns. By quantifying disease-related pattern expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. This chapter provides an overview of steps involved in pattern identification in 18F-FDG PET data and is divided into three sections. Section 1 introduces basic concepts in nuclear imaging and explores the cellular underpinnings of signals measured with 18F-FDG PET. Section 2 describes relevant basic concepts in 18F-FDG PET image analysis including anatomical registration, normalization, and analysis of variance and covariance. Section 3 is dedicated to SSM PCA specifically. The goal of this chapter is to make the technique more accessible to readers without a mathematics or neuroimaging background. Although many excellent texts on this topic exist, the current chapter aims to provide a more conceptual overview, including some discussion points that are not always formally described in literature.
|Titel||PET and SPECT in Neurology|
|Redacteuren||Rudi A. J. O. Dierckx, Andreas Otte, Erik F. J. de Vries, Aren van Waarde, Klaus L. Leenders|
|Uitgeverij||Springer International Publishing AG|
|ISBN van elektronische versie||9783030531683|
|ISBN van geprinte versie||9783030531676|
|Status||Published - 20-okt.-2020|