A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

Abstract

A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing interscan and intersubject variability. By training a binary linear SVM classifier with PET datasets from two different groups, potential pseudo-reference regions were identified by considering their regional average or total contribution to the classification score. This approach was evaluated in three cohorts with different brain PET tracers: (1) 11C-PiB PET scans of Alzheimer's disease (AD) patients and age-matched controls (OC); (2) baseline and blocking scans of an 11C-UCB-J PET occupancy study; and (3) 18F-DPA-714 PET scans for healthy controls (HC) and chemo-treated women with breast cancer (BC). In the first cohort, cerebellum, brainstem, and subcortical white matter were confirmed as pseudo-reference regions. The same regions were identified for the second cohort using either the VT maps or the SUV images. In the third cohort, cerebellum and brainstem were identified as pseudo-reference regions, alongside subcortical white matter and temporal cortex. In addition, the SVM-based approach demonstrated robust performance even with a reduced number of subjects, therefore confirming its applicability in identifying pseudo-reference regions without a priori assumptions and with only limited data across different PET tracers.