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Related Experiment Video

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Pierrick Bourgeat1, Jurgen Fripp1, Ashley G Gillman2

  • 1CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, QLD, Australia.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A novel deep learning method, DeepSUVR, enhances amyloid PET quantification by correcting Standardised Uptake Value Ratio (SUVR) noise and variability. This improves consistency across large datasets and different tracers for better clinical decision-making.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Biomarker Quantification

Background:

  • Centiloid (CL) quantification using Standardised Uptake Value Ratio (SUVR) is susceptible to noise, spill-in, and reference region binding.
  • Accurate amyloid PET quantification is crucial for diagnosing and monitoring Alzheimer's disease and evaluating treatment efficacy.

Purpose of the Study:

  • To develop and validate a novel deep learning method (DeepSUVR) for correcting SUVR quantification in amyloid PET imaging.
  • To assess DeepSUVR's performance in harmonizing large, multi-cohort datasets and improving longitudinal tracking of amyloid accumulation.

Main Methods:

  • A deep learning network (DeepSUVR) was trained on longitudinal data from 2,281 participants (7,380 scans) in the ADNI and AIBL cohorts.
  • The model learns scan-specific variability to predict SUVR correction factors, penalizing temporal deviations from established trends.
  • Validation was performed across seven large cohorts (8,806 participants, 12,320 scans), comparing DeepSUVR with standard Centiloid methods.

    • Main Results:

    • DeepSUVR improved the alignment and reduced variability of baseline Centiloid values across multiple studies and tracers.
    • DeepSUVR demonstrated stronger correlations with cognitive scores (MMSE) and better diagnostic group separation (CDR) compared to standard methods.
    • The method enhanced longitudinal model agreement and increased the effect size for treatment-induced changes in amyloid accumulation rates.

    Conclusions:

    • Deep learning, specifically DeepSUVR, represents a significant advancement in amyloid PET quantification, outperforming traditional methods.
    • DeepSUVR facilitates harmonization of large datasets and diverse PET tracers, crucial for consistent clinical decision-making and subtle intervention outcome detection.
    • This approach holds promise for improving the reliability of amyloid PET in both observational research and clinical trials.