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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.
Fundamental Principles of PET
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Machine learning positioning algorithms for long semi-monolithic scintillator PET detectors.

Samuel Mungai Kinyanjui1, Zhonghua Kuang2, Zheng Liu3

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging , Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Xili Shenzhen University Town, Nanshan District, Shenzhen, China, Shenzhen, Guangdong, 518055, CHINA.

Physics in Medicine and Biology
|May 21, 2025
PubMed
Summary

Machine learning algorithms significantly improved spatial resolution in semi-monolithic scintillator detectors. This advancement enhances accuracy in both y and z directions, crucial for advanced imaging applications.

Keywords:
PET detectorPositron emission tomography (PET)extreme gradient boosting (XGBoost)genetic algorithm (GA)machine learning algorithmparticle swarm optimization (PSO)semi-monolithic crystal

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Area of Science:

  • Nuclear Instrumentation
  • Medical Physics
  • Machine Learning Applications

Background:

  • Semi-monolithic scintillator detectors are vital for high-resolution imaging.
  • Existing analytical positioning methods have limitations in spatial resolution, particularly at detector ends.
  • Improving spatial resolution is key to advancing detector performance in various scientific fields.

Purpose of the Study:

  • To develop and evaluate machine learning positioning algorithms for semi-monolithic scintillator detectors.
  • To enhance spatial resolution in both the monolithic (y) and depth of interaction (z) directions.
  • To compare the performance of machine learning methods against traditional analytical techniques.

Main Methods:

  • Manufactured two semi-monolithic scintillator detectors using lutetium yttrium oxyorthosilicate (LYSO) slabs.
  • Utilized a 4x16 silicon photomultiplier array for readout.
  • Employed Extreme Gradient Boosting (XGBoost) machine learning models, optimized with Genetic Algorithm (GA) or Particle Swarm Optimization (PSO), to predict interaction positions.

Main Results:

  • Machine learning positioning methods significantly improved both y and z spatial resolutions compared to analytical methods.
  • Achieved average y spatial resolutions of 0.92 ± 0.41 mm and 0.94 ± 0.44 mm, outperforming the 1.38-1.39 mm of analytical methods.
  • Attained average z spatial resolutions of 1.67 ± 0.41 mm and 1.68 ± 0.45 mm, superior to the 2.09-2.14 mm from analytical methods.

Conclusions:

  • Machine learning algorithms, specifically XGBoost, offer superior spatial resolution for semi-monolithic scintillator detectors.
  • These algorithms achieve sub-millimeter y-spatial resolution (<1 mm) and sub-2 mm z-spatial resolution (<2 mm).
  • The developed methods represent a significant advancement for high-precision scintillator detector applications.