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

SUBMITTER: Tang C 

PROVIDER: S-EPMC11563559 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature