Development and Evaluation of a Dual-Layer-Offset PET Detector Constructed with Different Reflectors

Dual-layer-offset or multi-layer-offset design of a PET detector can improve spatial resolution while maintaining high sensitivity. In this study, three dual-layer-offset LYSO detectors with three different reflectors (ESR, Toray, and BaSO4) were developed. The top layer consisted of a 17 × 17 array of crystals 1 × 1 × 6.5 mm3 in size and the bottom layer consisted of an 18 × 18 array of crystals 1 × 1 × 9.5 mm3 in size. Neither light guides nor optical glue were used between the two layers of crystals. A custom-designed electronics system, composed of a 6 × 6 SiPM array, two FPC cables, and a custom-designed data processing module, was used to read out signals. An optimized interaction-decoding algorithm using the center of gravity to determine the position and threshold of analog signals for timing methods was applied to generate decoding flood histograms. The detector performances, in terms of peak to valley ratio of the flood histograms and energy resolutions, were calculated and compared. The dual-layer-offset PET detector constructed with BaSO4 reflectors performed much better than the other two reflectors in both crystal identification and energy resolution. The average peak-to-valley ratio and the energy resolution were approximately 7 and 11%, respectively. In addition, the crystals in the bottom layer showed better performance at crystal identification than those in the top layer. This study can act as a reference providing guidance in choosing scintillator reflectors for multi-layer dedicated DOI detectors designed for small-animal PET imaging.

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Depth of Interaction Estimation in a Preclinical PET Scanner Equipped with Monolithic Crystals Coupled to SiPMs Using a Deep Neural Network

Applied Sciences ◽

10.3390/app10144753 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4753

Author(s):

Amirhossein Sanaat ◽

Habib Zaidi

Keyword(s):

Neural Network ◽

Multilayer Perceptron ◽

Deep Neural Network ◽

Small Animal ◽

High Sensitivity ◽

Small Animal Pet ◽

Depth Of Interaction ◽

Scintillation Light ◽

Energy Position ◽

Silicon Photomultiplier

The scintillation light distribution produced by photodetectors in positron emission tomography (PET) provides the depth of interaction (DOI) information required for high-resolution imaging. The goal of positioning techniques is to reverse the photodetector signal’s pattern map to the coordinates of the incident photon energy position. By considering the DOI information, monolithic crystals offer good spatial, energy, and timing resolution along with high sensitivity. In this work, a supervised deep neural network was used for the approximation of DOI and to assess through Monte Carlo (MC) simulations the performance on a small-animal PET scanner consisting of ten 50 × 50 × 10 mm3 continuous Lutetium-Yttrium Oxyorthosilicate doped with Cerium (LYSO: Ce) crystals and 12 × 12 silicon photomultiplier (SiPM) arrays. The scintillation position was predicted by a multilayer perceptron neural network with 256 units and 4 layers whose inputs were the number of fired pixels on the SiPM plane and the total deposited energy. A GEANT4 MC code was used to generate training and test datasets by altering the photons’ incident position, energy, and direction, as well as readout of the photodetector output. The calculated spatial resolutions in the X-Y plane and along the Z-axis were 0.96 and 1.02 mm, respectively. Our results demonstrated that using a multilayer perceptron (MLP)-based positioning algorithm in the detector modules, constituting the PET scanner, enhances the spatial resolution by approximately 18% while the absolute sensitivity remains constant. The proposed algorithm proved its ability to predict the DOI for depth under 7 mm with an error below 8.7%.

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