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We propose a graph-based framework for simulating dPET time-activity curves, combining structural and functional multimodal neuroimaging data. Our method characterises both local kinetics and network-mediated propagation of the signal by representing the brain as a weighted graph and modelling the tracer dynamics with a reaction-diffusion model with a source term. The framework optimises the model parameters to reproduce observed time-activity curves, visualising both the simulated signal and the terms of the representing model for personalised brain characterisation.

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Graph-Based Analysis of dPET Time-Activity Curves with Multimodal Data

  • 会议论文

  • 发表日期：

    2026年

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  • Simone Cammarasana,
  • Giuseppe Patanè

摘要

We propose a graph-based framework for simulating dPET time-activity curves, combining structural and functional multimodal neuroimaging data. Our method characterises both local kinetics and network-mediated propagation of the signal by representing the brain as a weighted graph and modelling the tracer dynamics with a reaction-diffusion model with a source term. The framework optimises the model parameters to reproduce observed time-activity curves, visualising both the simulated signal and the terms of the representing model for personalised brain characterisation.