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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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TAU PET HARMONIZATION VIA SURFACE-BASED DIFFUSION MODEL.

Jiaxin Yue1,2, Jianwei Zhang1,2, Lujia Zhong1,2

  • 1Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

Harmonizing multi-site tau positron emission tomography (PET) data is crucial for large datasets. This study introduces a novel framework using surface-based preprocessing and diffusion models to effectively harmonize tau PET imaging data across different tracers and sites.

Keywords:
Harmonizationdiffusion modeltau PET

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Area of Science:

  • Neuroimaging
  • Medical Imaging Analysis
  • Biostatistics

Background:

  • Tau positron emission tomography (PET) imaging is vital for Alzheimer's disease research.
  • Heterogeneity in multi-site tau PET data across tracers hinders data integration and analysis.
  • Existing harmonization methods are inadequate due to complex tracer binding properties and artifacts.

Purpose of the Study:

  • To develop and validate a systematic framework for harmonizing multi-site tau PET data.
  • To address the challenges posed by tracer heterogeneity and artifacts in tau PET imaging.
  • To enable more robust utilization of large-scale, multi-site tau PET datasets.

Main Methods:

  • A novel harmonization framework integrating surface-based data preprocessing and a diffusion model.
  • Generation of vertex-wise mapping for standardized uptake value ratio (SUVR) on the cortical surface.
  • Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Disparities (HABS-HD).

Main Results:

  • The proposed method successfully harmonized tau PET data from multiple sites and tracers.
  • Generated SUVR maps exhibited consistent pattern distributions post-harmonization.
  • The framework preserved individual variability while ensuring data comparability.

Conclusions:

  • The developed systematic framework effectively harmonizes multi-site tau PET data.
  • This approach overcomes limitations of existing methods by accounting for tracer-specific binding patterns.
  • Enables reliable integration and analysis of diverse tau PET datasets for advancing Alzheimer's disease research.