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A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators.

Pietro Carra1,2, Maria Giuseppina Bisogni1,2, Esther Ciarrocchi1,2

  • 1Università di Pisa, Dipartimento di Fisica E. Fermi, Italy.

Physics in Medicine and Biology
|May 24, 2022
PubMed
Summary
This summary is machine-generated.

New AI algorithms enable monolithic crystals in PET scanners to achieve superior spatial and timing resolution, rivaling pixellated crystals for time-of-flight imaging.

Keywords:
PET detectorsartificial intelligenceevent positioningevent timestampingmonolithic scintillatorsneural networkssilicon photomultipliers

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Area of Science:

  • Medical Physics
  • Nuclear Instrumentation
  • Artificial Intelligence in Imaging

Background:

  • Monolithic scintillator crystals offer excellent spatial and depth-of-interaction (DOI) resolution for Positron Emission Tomography (PET).
  • Historically, monolithic crystals have lagged behind pixellated crystals in timing resolution and real-time processing capabilities.
  • Advanced algorithms are needed to unlock the full potential of monolithic detectors in clinical PET.

Purpose of the Study:

  • To develop and evaluate novel algorithms for enhancing spatial and timing resolution in monolithic scintillator PET detectors.
  • To enable real-time processing of PET events using artificial intelligence on integrated hardware.
  • To demonstrate that monolithic crystals can achieve performance competitive with or exceeding pixellated crystals for time-of-flight (TOF) PET.

Main Methods:

  • Developed a two-stage neural network approach for event timestamping and simultaneous position/timestamp estimation.
  • Integrated lightweight neural networks onto a low-cost field-programmable gate array (FPGA) for real-time processing (>1 million events/sec).
  • Validated algorithms using both simulation frameworks and experimental data from a monolithic LYSO crystal coupled to Silicon Photomultiplier (SiPM) arrays.

Main Results:

  • Achieved 0.78 mm 2D Full Width at Half Maximum (FWHM) spatial resolution and 1.2 mm DOI resolution.
  • Obtained a coincidence time resolution of 156 picoseconds (ps).
  • Demonstrated real-time processing capability directly within the detector.

Conclusions:

  • Monolithic crystals, enhanced by AI algorithms, can match or surpass pixellated crystals in TOF PET performance.
  • The developed AI-driven approach offers superior spatial and DOI resolution compared to traditional methods.
  • On-detector event characterization using lightweight neural networks overcomes previous limitations in scalability and computational complexity for monolithic PET detectors.