Brain inflammation is associated with various types of neurodegen-erative diseases, including Alzheimer disease (AD). Quantifying inflammation with PET is a challenging and invasive procedure, especially in frail patients, because it requires blood sampling from an arterial catheter. A widely used alternative to arterial sampling is a supervised clustering algorithm (SVCA), which identifies the voxels with minimal specific binding in the PET images, thus extracting a reference region for noninvasive kinetic modeling. Methods: We tested this algorithm on a large population of subjects injected with the translocator protein radioligand 11C-PBR28 and compared the kinetic modeling results obtained with the gold standard of arterial input function (VT/fp) with those obtained by SVCA (distribution volume ratio [DVR] with Logan plot). The study comprised 57 participants (21 healthy controls, 11 mild cognitive impairment patients, and 25 AD patients). Results: We found that VT/fp was greater in AD patients than in controls in the inferior parietal, combined middle and inferior temporal, and entorhinal cortices. SVCA-DVR identified increased binding in the same regions and in an additional one, the parahippocampal region. We noticed however that the average amplitude of the reference curve obtained from subjects with genetic high-affinity binding for 11C-PBR28 was significantly larger than that from subjects with moderate affinity. This suggests that the reference curve extracted by SVCA was contaminated by specific binding. Conclusion: SVCA allows the noninvasive quantification of inflammatory biomarker translocator protein measured with 11C-PBR28 but without the need of arterial sampling. Although the reference curves were contaminated with specific binding, the decreased variance of the outcome measure, SVCA DVR, allowed for an apparent greater sensitivity to detect regional abnormalities in brains of patients with AD.
Bibliographical noteFunding Information:
Dr. Lyoo was financially supported by the Basic Science Research Program through the National Research Foundation of Korea grant funded by the Ministry of Science, ICT & Future Planning (2017R1A2B2006694). No other potential conflict of interest relevant to this article was reported.
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All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging