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

• Tác giả: Sabine Deprez, Michel Koole, Gwen Schroyen, Chunmeng Tang, Koen Van Laere, Greet Vanderlinden
• Ngôn ngữ: eng
• Ký hiệu phân loại: 004.357 Specific multiprocessor computers
• Thông tin xuất bản: United States : Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism , 2025
• Bộ sưu tập: NCBI
• ID: 251533

Tóm tắt

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)