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Updated: Jul 31, 2025

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
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A graphical user interface for calculating the arterial input function during dynamic positron emission tomography.

Youstina Daoud1,2, Liam Carroll1,2, Shirin A Enger1,2

  • 1Medical Physics Unit, Department of Oncology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.

Physics in Medicine and Biology
|May 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new graphical user interface (GUI) for non-invasively calculating the arterial input function (AIF) in dynamic positron emission tomography (dPET) using patient-specific wrist models. The developed toolkit enhances dPET simulations and personalized AIF calculations.

Keywords:
Monte Carlo simulation toolkitarterial input functiondynamic positron emission tomographygraphical user interface

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Computational Modeling

Background:

  • Dynamic positron emission tomography (dPET) traditionally requires invasive arterial blood sampling for arterial input function (AIF) acquisition.
  • Non-invasive AIF calculation methods are crucial for improving dPET safety and accessibility.
  • Previous work developed a novel detector and Monte Carlo simulation software for non-invasive AIF estimation.

Purpose of the Study:

  • To develop a graphical user interface (GUI) for creating patient-specific wrist phantoms using ultrasound scans.
  • To enable the measurement of wrist features necessary for calculating the AIF non-invasively.
  • To integrate ultrasound-based phantoms into Geant4 Monte Carlo simulations for dPET applications.

Main Methods:

  • Implemented a GUI using Qt5 and VTK-8.2.0 for importing and analyzing 2D ultrasound wrist scans.
  • Users measure radial artery and vein dimensions to construct a patient-specific wrist phantom.
  • Simulated 100 million decays of 18F and 68Ga using the patient-specific phantom in Geant4 Monte Carlo software.

Main Results:

  • Detector efficiency decreased by 3.5% for 18F and 51.7% for 68Ga with patient-specific phantoms compared to generic ones.
  • The clinical data processing algorithm showed accuracy errors greater than 1.0% for both radioisotopes when using patient-specific phantoms.
  • Patient-specific phantom integration led to more precise simulations of the developed dPET detector.

Conclusions:

  • The developed toolkit facilitates Geant4 Monte Carlo simulations for dPET detector development using patient-specific wrist phantoms.
  • This approach allows for more accurate simulation of detectors during dPET scans.
  • Enables the calculation of a personalized arterial input function (AIF) for improved dPET analysis.