Software-Based Real-Time Acquisition and Processing of PET Detector Raw Data.

In modern positron emission tomography (PET) readout architectures, the position and energy estimation of scintillation events (singles) and the detection of coincident events (coincidences) are typically carried out on highly integrated, programmable printed circuit boards. The implementation of advanced singles and coincidence processing (SCP) algorithms for these architectures is often limited by the strict constraints of hardware-based data processing. In this paper, we present a software-based data acquisition and processing architecture (DAPA) that offers a high degree of flexibility for advanced SCP algorithms through relaxed real-time constraints and an easily extendible data processing framework. The DAPA is designed to acquire detector raw data from independent (but synchronized) detector modules and process the data for singles and coincidences in real-time using a center-of-gravity (COG)-based, a least-squares (LS)-based, or a maximum-likelihood (ML)-based crystal position and energy estimation approach (CPEEA). To test the DAPA, we adapted it to a preclinical PET detector that outputs detector raw data from 60 independent digital silicon photomultiplier (dSiPM)-based detector stacks and evaluated it with a [(18)F]-fluorodeoxyglucose-filled hot-rod phantom. The DAPA is highly reliable with less than 0.1% of all detector raw data lost or corrupted. For high validation thresholds (37.1 ± 12.8 photons per pixel) of the dSiPM detector tiles, the DAPA is real time capable up to 55 MBq for the COG-based CPEEA, up to 31 MBq for the LS-based CPEEA, and up to 28 MBq for the ML-based CPEEA. Compared to the COG-based CPEEA, the rods in the image reconstruction of the hot-rod phantom are only slightly better separable and less blurred for the LS- and ML-based CPEEA. While the coincidence time resolution (∼ 500 ps) and energy resolution (∼12.3%) are comparable for all three CPEEA, the system sensitivity is up to 2.5 × higher for the LS- and ML-based CPEEA.