{'fraction_non_alphanumeric': 0.01985906470211403, 'fraction_numerical': 0.0012812299807815502, 'mean_word_length': 5.703862660944206, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Study on listmode OSEM reconstruction including image-space resolution recovery techniques for Compton camera

Although Compton camera may has a great potential as next generation imaging modality comparing to SPECT and PET, its fully three-dimensional image reconstruction requires the considerable computational burden and the spatial resolution is suffered from the various physical phenomena arising during detection process. In this study, we investigated the accelerated statistical image reconstruction in which system matrix included a resolution recovery (RR) technique. We considered 3D Gaussian resolution model for the integrated angular and geometric uncertainties. The angular uncertainty is closely related to the limited energy resolution of the Compton camera and Doppler broadening and the geometric uncertainty is due to the segmented detectors. For RR, the 3D Gaussian resolution model is incorporated into listmode OSEM (LMOSEM) using image-space convolution operation. We investigated two different RR approaches: one (denoted by LMOSEM-RRF) is when the convolution is only performed in forward projection step, and the other (denoted by LMOSEM-RRFB) is when it is performed in both forward and backward projection steps. The simulation results showed that both RR approaches gave an improvement on spatial resolution for the resolution-degraded data due to both angular and geometric uncertainties. Although LMOSEM-RRF provided better resolution than LMOSEM-RRFB, LMOSEM-RRFB could still useful for low counting statistics in measurement.