Dataset Information

Automatic Extraction of a Reference Region for the Noninvasive Quantification of Translocator Protein in Brain Using 11C-PBR28.

ABSTRACT: Brain inflammation is associated with various types of neurodegenerative 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 (V T/f p) 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 V T/f p 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.

SUBMITTER: Zanotti-Fregonara P

PROVIDER: S-EPMC6604696 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Automatic Extraction of a Reference Region for the Noninvasive Quantification of Translocator Protein in Brain Using <sup>11</sup>C-PBR28.

Zanotti-Fregonara Paolo P Kreisl William C WC Innis Robert B RB Lyoo Chul Hyoung CH

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20190117 7

Brain inflammation is associated with various types of neurodegenerative 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 noninvas ...[more]

PMID: 30655330

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Project description:BackgroundThe standard reference region (RR) for amyloid-beta (A?) PET studies is the cerebellar grey matter (GMCB), while alternative RRs have mostly been utilized without prior validation against the gold standard. This study compared five commonly used RRs to gold standard plasma input-based quantification using the GMCB.MethodsThirteen subjects from a test-retest (TRT) study and 30 from a longitudinal study were retrospectively included (total: 17 Alzheimer's disease, 13 mild cognitive impairment, 13 controls). Dynamic [11C]PiB PET (90 min) and T1-weighted MR scans were co-registered and time-activity curves were extracted for cortical target regions and the following RRs: GMCB, whole cerebellum (WCB), white matter brainstem/pons (WMBS), whole brainstem (WBS) and eroded subcortical white matter (WMES). A two-tissue reversible plasma input model (2T4k_Vb) with GMCB as RR, reference Logan and the simplified reference tissue model were used to derive distribution volume ratios (DVRs), and standardized uptake value (SUV) ratios were calculated for 40-60 min and 60-90 min intervals. Parameter variability was evaluated using TRT scans, and correlations and agreements with the gold standard (DVR from 2T4k_Vb with GMCB RR) were also assessed. Next, longitudinal changes in SUVs (both intervals) were assessed for each RR. Finally, the ability to discriminate between visually A? positive and A? negative scans was assessed.ResultsAll RRs yielded stable TRT performance (max 5.1% variability), with WCB consistently showing lower variability. All approaches were able to discriminate between A? positive and A? negative scans, with highest effect sizes obtained for GMCB (range?-?0.9 to?-?0.7), followed by WCB (range?-?0.8 to?-?0.6). Furthermore, all approaches provided good correlations with the gold standard (r???0.78), while the highest bias (as assessed by the regression slope) was observed using WMES (range slope 0.52-0.67), followed by WBS (range slope 0.58-0.92) and WMBS (range slope 0.62-0.91). Finally, RR SUVs were stable across a period of 2.6 years for all except WBS and WMBS RRs (60-90 min interval).ConclusionsGMCB and WCB are considered the best RRs for quantifying amyloid burden using [11C]PiB PET.

Dataset Information

Automatic Extraction of a Reference Region for the Noninvasive Quantification of Translocator Protein in Brain Using 11C-PBR28.

Publications

Automatic Extraction of a Reference Region for the Noninvasive Quantification of Translocator Protein in Brain Using <sup>11</sup>C-PBR28.

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