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Performance evaluation of side-by-side optically coupled monolithic LYSO crystals.

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Summary

Machine learning significantly improves detector performance in monolithic positron emission tomography (PET) scanners by reducing edge effects. This advancement enables the creation of more sensitive and cost-effective PET systems with minimal gaps between detectors.

Keywords:
PETmonolithic scintillatorneural networkoptical couplingposition estimation

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Area of Science:

  • Medical Imaging
  • Nuclear Instrumentation
  • Materials Science

Background:

  • Monolithic scintillation crystals offer cost and sensitivity advantages over pixellated designs in Positron Emission Tomography (PET) scanners.
  • Monolithic designs enable depth-of-interaction measurement but suffer from edge effects that degrade positioning accuracy.
  • Edge effects, caused by light distribution truncation at crystal borders, are a key challenge in monolithic PET detector performance.

Purpose of the Study:

  • To experimentally validate performance enhancements using machine learning (ML) artificial neural networks (NNs) for positioning estimation in monolithic scintillators.
  • To evaluate the impact of optical coupling methods on detector performance in side-by-side monolithic scintillator configurations.

Main Methods:

  • Two LYSO monolithic crystals were optically coupled using high refractive index Meltmount (n=1.70), optical grease (n=1.46), or standard black paint with an air gap.
  • A 12x12 silicon photomultiplier array served as the photosensor for evaluating detector configurations.
  • Positioning accuracy was assessed using a squared-charge (SC) centroid technique and a machine-learning artificial neural network (NN) algorithm.

Main Results:

  • The NN algorithm improved spatial resolution at the crystal interface from 1.7 ± 0.3 mm (SC, Meltmount) to 1.0 ± 0.2 mm.
  • Similar improvements were observed for optical grease and standard configurations, with NN resolutions of 1.2 ± 0.2 mm and 1.2 ± 0.3 mm, respectively.
  • Energy resolutions at the interface ranged from 18 ± 2% (Meltmount) to 23 ± 3% (standard configuration), with NN showing improved performance.

Conclusions:

  • High refractive index optical coupling combined with NN algorithms effectively mitigates edge effects in monolithic PET detectors.
  • This approach facilitates the development of PET scanners with improved detector packing density and enhanced performance.
  • The study demonstrates a viable method for overcoming limitations in monolithic scintillator-based imaging systems.