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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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Compensating Positron Range Effects of Ga-68 in Preclinical PET Imaging by Using Convolutional Neural Network: A

Ching-Ching Yang1,2

  • 1Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

Diagnostics (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

Convolutional neural network (CNN) model CNN3 shows feasibility for positron range correction in Gallium-68 (Ga-68) preclinical PET imaging. This method enhances image sharpness and improves quantitative accuracy, making CNN3 a promising tool for Ga-68 PET analysis.

Keywords:
Ga-68 preclinical PET imagingconvolutional neural networkpositron range correction

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Artificial Intelligence

Background:

  • Positron range effects in preclinical PET imaging can degrade image quality and quantitative accuracy.
  • Gallium-68 (Ga-68) is a widely used positron-emitting radionuclide in PET imaging.
  • Accurate positron range correction is crucial for reliable Ga-68 PET studies.

Purpose of the Study:

  • To evaluate the feasibility of three different convolutional neural network (CNN) models for positron range correction in Ga-68 preclinical PET imaging.
  • To compare the performance of CNN models originally designed for super-resolution and pseudo-CT synthesis.
  • To identify the most suitable CNN architecture for improving Ga-68 PET image quality.

Main Methods:

  • Monte Carlo simulations were used to model a preclinical PET scanner and 30 phantom configurations for Ga-68 and 511-keV gamma rays.
  • Three CNN models (CNN1, CNN2, CNN3) were investigated for their ability to perform positron range correction.
  • Euclidean distance served as the loss function to train the CNN models, minimizing the difference between input and output images.

Main Results:

  • CNN3 demonstrated superior performance over CNN1 and CNN2 in both qualitative and quantitative assessments.
  • Positron range correction using CNN3 resulted in sharper image boundaries for Ga-68.
  • Quantitative analysis showed improved recovery coefficients (RC) and spill-over ratios (SOR) without significant increases in their variability.

Conclusions:

  • CNN3 is a promising architecture for implementing positron range correction in Ga-68 preclinical PET imaging.
  • The developed CNN-based correction method enhances image quality and quantitative accuracy.
  • This approach holds potential for advancing preclinical PET research using Ga-68.