Sensitivity Increase Through a Neural Network Method for LOR Recovery of ICS Triple Coincidences in High-Resolution Pixelated- Detectors PET Scanners

Abstract

Scanner sensitivity is often critical in high-resolution Positron Emission Tomography (PET) dedicated to molecular imaging. In neighboring pixelated detectors with individual readout, sensitivity decreases because of multiple coincidences produced by Compton scattering. Correct analysis of those coincidences would enable a substantial sensitivity increase. However, including scattering byproducts in the image often lead to image quality degradation because of inaccurate Line-of-Response (LOR) assessment. In such scanners, to support high count rates, multiple coincidences are usually discarded when image degradation is not acceptable, or blindly accepted for a low computational burden. This paper presents a new, real-time capable method that includes Inter-Crystal Scatter (ICS) triple coincidences in the image without significant quality degradation. The method computes the LOR using a neural network fed by preprocessed raw data. As a proof of principle, this paper analyzes the simplest ICS scenario, triple coincidences where one photoelectric 511-keV event coincides with two more whose energy sum is also 511 keV. The paper visits the algorithm structure, presents Monte Carlo assessment with the LabPET model, and displays images reconstructed from real data. With an energy window of 360-660 keV and a singles energy threshold of 125 keV, the inclusion of triple coincidences yielded a sensitivity increase of 54%, a resolution degradation similar to that of other sensitivity-increasing methods, and only a slight contrast degradation for real LabPET data, with potential for numerous further improvements.