Validating the Utility of Supervised Clustering Algorithm for Precise [ 11 C]DPA-713 PET Brain Image Quantification

The reliance of quantitative PET imaging on the arterial input function makes brain PET challenging to perform in certain populations, limiting the sample size. To address this challenge, a supervised clustering algorithm (SVCA) has been introduced as an alternative. Our objective was to validate SV...

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Veröffentlicht in:The Journal of nuclear medicine (1978) Jg. 66; H. 5; S. 764
Hauptverfasser: Lee, Youjin, Nguyen, Thanh D, Du, Yong, Coughlin, Jennifer M, Zein, Sara A, Karakatsanis, Nicolas A, Nehmeh, Sadek, Pomper, Martin G, Gauthier, Susan A, Kang, Yeona
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.05.2025
Schlagworte:
Adult
Algorithms
Brain - diagnostic imaging
Carbon Radioisotopes
Cluster Analysis
Clustering Algorithms
Female
Humans
Image Processing, Computer-Assisted - methods
Male
Middle Aged
Positron-Emission Tomography - methods
Pyrazoles
Pyridines
Pyrimidines
Pyrrolidinones
supervised clustering algorithm
[11C]DPA-713
neuroinflammation
multiple sclerosis
PET
ISSN:1535-5667
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Zusammenfassung:The reliance of quantitative PET imaging on the arterial input function makes brain PET challenging to perform in certain populations, limiting the sample size. To address this challenge, a supervised clustering algorithm (SVCA) has been introduced as an alternative. Our objective was to validate SVCA's performance for brain PET with [ C]DPA-713 that targets a putative marker of brain injury and repair. This study included a composite dataset comprising 12 healthy volunteers (HVs), with 6 participants from Weill Cornell Medicine and 6 participants from Johns Hopkins University School of Medicine. The minimum number of subjects required to define kinetic classes was identified. Next, the distribution volume ratio (DVR) was examined by comparing pseudoreference time-activity curves derived from SVCA (SVCA-DVR) with the conventional arterial input function-based DVR (AIF-DVR). Test-retest analysis was conducted to evaluate repeatability, considering volumes of interest (VOIs) of various sizes. Lastly, the research investigated differences in DVR values between the HVs and patients with multiple sclerosis. The number of subjects necessary for the kinetic classes, which are critical to SVCA, was reduced to 7 from the existing minimum requirement of 10. This allowed for a more substantial independent validation within a defined dataset. Correlative analysis between SVCA-DVR and AIF-DVR demonstrated a strong relationship, with correlation coefficients of 0.86 for white matter and 0.95 for the thalamus. Furthermore, we noted a marked decline in absolute test-retest variability for SVCA-DVR, with reductions from 1.31% to 1.18% in white matter and 3.51% to 2.32% in the thalamus, relative to AIF-DVR. This pattern of reduced variability persisted across VOIs of disparate sizes, with the absolute test-retest variability remaining below 5% for SVCA-DVR, even in small VOIs (both high and low binding at 0.065 cm ). Analysis revealed a pronounced disparity in SVCA-DVR values of the thalamus when comparing HVs and patients with multiple sclerosis. The findings substantiate the pseudoreference time-activity curves derived from SVCA as a dependable and practical substitute for the quantification of [ C]DPA-713 PET scans of the brain.
ISSN:1535-5667
DOI:10.2967/jnumed.124.268519