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Positron Emission Tomography01:29

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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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Position estimation using neural networks in semi-monolithic PET detectors.

M Freire1, J Barrio1, N Cucarella1

  • 1Instituto de Instrumentación para Imagen Molecular (I3M), Centro Mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, E-46022 Valencia, Spain.

Physics in Medicine and Biology
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

This study compares crystal treatments and silicon photomultiplier (SiPM) models for total-body positron emission tomography (TB-PET) detectors. Results show promising spatial and energy resolution, indicating suitability for advanced TB-PET scanner development.

Keywords:
PETmachine learningneural networkposition estimationsemi-monolithic detectortotal body PET

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Area of Science:

  • Nuclear physics
  • Medical imaging technology
  • Detector physics

Background:

  • Total-body positron emission tomography (TB-PET) requires high-resolution detectors for improved imaging.
  • Optimizing detector performance involves careful selection of crystal materials, surface treatments, and photodetectors.
  • Semi-monolithic detector designs offer potential advantages for TB-PET scanner construction.

Purpose of the Study:

  • To experimentally compare the 3D spatial and energy resolution of semi-monolithic detectors for TB-PET scanners.
  • To evaluate the impact of different crystal surface treatments (Enhanced Specular Reflector (ESR) with/without black paint and retroreflector) on detector performance.
  • To assess the performance of two different silicon photomultiplier (SiPM) models (S13361-3050AE-08 and S14161-3050AS-08).

Main Methods:

  • Utilized an array of lutetium yttrium oxyorthosilicate (LYSO) slabs coupled to SiPM arrays.
  • Investigated three crystal surface treatments: ESR + Retroreflector (RR) + Black paint (B), ESR + RR, and All ESR.
  • Employed a neural network (NN) for estimating impact position in x and depth-of-interaction (DOI) directions, using data acquired with a 22Na source and pinhole collimator.

Main Results:

  • Energy resolution varied between 11 ± 1% and 16 ± 1% depending on the SiPM model and crystal treatment.
  • Positioning accuracy in the x-direction showed a mean average error of 1.1 ± 0.5 to 1.3 ± 0.5 mm.
  • Depth-of-interaction (DOI) resolution achieved a mean average error of 1.7 ± 0.8 to 2.2 ± 1.0 mm, irrespective of the SiPM model.

Conclusions:

  • The tested semi-monolithic detectors demonstrate competitive spatial and energy resolution suitable for TB-PET applications.
  • Crystal surface treatment significantly influences energy resolution, with the ESR + RR configuration showing the best performance.
  • These findings support the use of these detectors in the development of next-generation TB-PET scanners.