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PET AIF estimation when available ROI data is impacted by dispersive and/or background effects.

Finbarr O'Sullivan1

  • 1Department of Statistics, University College Cork, Cork, Ireland.

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Summary

This study introduces a new method using impulse response (IR) to accurately extract the arterial input function (AIF) from PET scan data, improving accuracy even with background noise.

Keywords:
PETarterial input functionconstraintsdispersionimpulse responseregularizationspillover

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging Physics
  • Biomedical Engineering

Background:

  • Positron Emission Tomography (PET) imaging relies on accurate arterial input function (AIF) extraction.
  • Blood pool region of interest (ROI) data in PET can be compromised by dispersion and background effects.
  • These artifacts hinder precise AIF signal quantification, impacting downstream kinetic modeling.

Purpose of the Study:

  • To develop a novel, data-adaptive method for estimating the arterial input function (AIF).
  • To address challenges in AIF extraction caused by dispersive and background effects in PET ROI data.
  • To improve the accuracy and reliability of AIF estimation in PET studies.

Main Methods:

  • Representing the AIF using the whole-body impulse response (IR) to the injection profile.
  • Analyzing a population of directly sampled arterial data to characterize tracer IR statistical behavior.
  • Developing a penalty term for regularized AIF estimation using quadratic programming.

Main Results:

  • The impulse response (IR)-based method demonstrates computational efficiency.
  • Evaluated with eight tracers in PET cancer imaging, including FDG, FLT, CO2, and H2O.
  • Simulations show significantly reduced mean squared error compared to direct ROI methods, even with minor contamination.

Conclusions:

  • The proposed IR-based AIF extraction scheme provides a practical solution for contaminated PET data.
  • This method enhances the accuracy of AIF estimation in the presence of dispersion and background effects.
  • It offers a robust approach for quantitative PET imaging, particularly in oncology.