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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.
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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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Monte Carlo-based scatter correction for the plastic-based modular PET scanner J-PET.

Reimund Bayerlein1,2, Manish Das3,4,5, Sushil Sharma3,4,5

  • 1Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America.

Physics in Medicine and Biology
|March 19, 2026
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Summary

A new Monte Carlo-based scatter correction method significantly improves image quality for the Jagiellonian Positron Emission Tomography (J-PET) scanner. This advancement enhances quantitative performance, bringing plastic scintillator PET closer to clinical relevance.

Keywords:
J-PET scannerMonte Carlo (MC) simulationimage quality assessmentplastic scintillatorsscatter correction (SC)

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Physics

Background:

  • Positron Emission Tomography (PET) imaging is crucial for quantitative diagnostics.
  • Scatter events in PET images degrade image quality and quantitative accuracy.
  • The Jagiellonian PET (J-PET) scanner utilizes plastic scintillators, presenting unique challenges for scatter correction.

Purpose of the Study:

  • To develop and evaluate a Monte Carlo (MC)-based scatter correction (SC) method for the J-PET scanner.
  • To assess the impact of SC on image quality metrics and quantitative accuracy.
  • To explore computational acceleration using surrogate materials in simulations.

Main Methods:

  • Implemented an MC-based SC algorithm within a time-of-flight ordered-subsets expectation maximization (TOF-OSEM) reconstruction framework.
  • Utilized SimSET for MC simulations.
  • Evaluated the method using NEMA IQ phantom and human subject data on the J-PET scanner.
  • Assessed LYSO crystals as a computational surrogate for BC-404 plastic scintillators in simulations.

Main Results:

  • SC improved contrast recovery coefficients (CRC) by over 20% and reduced background variability (BGVar) by 8.5% in phantom studies.
  • Residual activity in cold regions was reduced, and organ delineation was improved in human scans.
  • Simulations with LYSO reduced runtime by nearly an order of magnitude with <6% deviation in image quality metrics compared to BC-404.
  • Quantitative comparison with a commercial PET/CT scanner showed consistent activity concentration ratios, though J-PET exhibited higher noise and residual scatter.

Conclusions:

  • The developed MC-based SC method effectively removes scatter for the J-PET scanner.
  • The SC method enhances quantitative performance, enabling more accurate PET imaging.
  • This work provides a foundation for advanced correction techniques and advances the J-PET scanner towards clinical applicability.