Experimental evaluation of convolutional neural network-based inter-crystal scattering recovery for high-resolution PET detectors
Seung‐Eun Lee, Jae Sung Lee
Abstract
Abstract Objective . One major limiting factor for achieving high resolution of positron emission tomography (PET) is a Compton scattering of the photon within the crystal, also known as inter-crystal scattering (ICS). We proposed and evaluated a convolutional neural network (CNN) named ICS-Net to recover ICS in light-sharing detectors for real implementations preceded by simulations. ICS-Net was designed to estimate the first-interacted row or column individually from the 8 × 8 photosensor amplitudes. Approach . We tested 8 × 8, 12 × 12, and 21 × 21 Lu 2 SiO 5 arrays with pitches of 3.2, 2.1, and 1.2 mm, respectively. We first performed simulations to measure the accuracies and error distances, comparing the results to previously studied pencil-beam-based CNN to investigate the rationality of implementing fan-beam-based ICS-Net. For experimental implementation, the training dataset was prepared by obtaining coincidences between the targeted row or column of the detector and a slab crystal on a reference detector. ICS-Net was applied to the detector pair measurements with moving a point source from the edge to center using automated stage to evaluate their intrinsic resolutions. We finally assessed the spatial resolution of the PET ring. Main results . The simulation results showed that ICS-Net improved the accuracy compared with the case without recovery, reducing the error distance. ICS-Net outperformed a pencil-beam CNN, which provided a rationale to implement a simplified fan-beam irradiation. With the experimentally trained ICS-Net, the degree of improvements in intrinsic resolutions were 20%, 31%, and 62% for the 8 × 8, 12 × 12, and 21 × 21 arrays, respectively. The impact was also shown in the ring acquisitions, achieving improvements of 11%–46%, 33%–50%, and 47%–64% (values differed from the radial offset) in volume resolutions of 8 × 8, 12 × 12, and 21 × 21 arrays, respectively. Significance . The experimental results demonstrate that ICS-Net can effectively improve the image quality of high-resolution PET using a small crystal pitch, requiring a simplified setup for training dataset acquisition.