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

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 V_T 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.