Experimental evaluation of convolutional neural network-based inter-crystal scattering recovery for high-resolution PET detectors.

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 Lu2SiO5 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.