From Positron to Pattern: A Conceptual and Practical Overview of 18F-FDG PET Imaging and Spatial Covariance Analysis

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.