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Positron Emission Tomography

Positron Emission Tomography

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.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...

Imaging Studies II: Positron Emission Tomography and Scintigraphy

Imaging Studies II: Positron Emission Tomography and Scintigraphy

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.
Fundamental Principles of PET

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Related Experiment Video

Updated: Jun 15, 2025

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET

Matteo Ferrante¹, Marianna Inglese¹, Ludovica Brusaferri²

¹Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Rome, Italy.

Computer Methods and Programs in Biomedicine

|August 24, 2024

Summary

This summary is machine-generated.

This study introduces a novel non-invasive method for quantifying dynamic PET imaging data by predicting the arterial input function (AIF) using deep learning. The approach accurately estimates tracer concentrations and distribution volumes, improving PET scan utility.

Keywords:

AIF IDIF Metabolic imaging PET Physics informed neural networks TSPO

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Published on: June 7, 2024

Area of Science:

Neuroimaging
Radiochemistry
Computational Biology

Background:

Dynamic Positron Emission Tomography (PET) imaging requires accurate quantification of tracer kinetics.
Traditional methods for determining the arterial input function (AIF) are invasive, requiring arterial cannulation.
Non-invasive AIF quantification is crucial for broader PET application.

Purpose of the Study:

To develop and validate a novel, non-invasive method for quantifying dynamic PET imaging data.
To accurately predict the arterial input function (AIF) without invasive procedures.
To enable precise prediction of tracer concentrations and distribution volumes.

Main Methods:

Utilized a deep neural network with 3D depth-wise separable convolutional layers.
Incorporated a priori knowledge of AIF functional form and shape.
Predicted [11C]PBR28 concentrations in whole blood and plasma.

Main Results:

Achieved high cross-validated Pearson correlations (0.86 for whole blood, 0.89 for plasma) between predicted and invasively measured AIFs.
Demonstrated accurate estimation of distribution volumes using predicted AIFs in a two-tissue compartmental model.
Captured biological variability related to sex, TSPO binding affinity, and age.

Conclusions:

The proposed method provides accurate and reliable non-invasive quantification of dynamic PET data.
This streamlined approach enhances the utility of PET imaging in clinical research and diagnostics.
The technique offers a promising alternative to invasive AIF measurements for various tracers.