Exploring spatiotemporal information in a Cherenkov and scintillation photon counting BGO TOF-PET semi-monolithic...

Objective. Cherenkov signatures from a bismuth germanate (BGO) crystal open the possibility of establishing BGO as a promising material for time-of-flight positron emission tomography (TOF-PET) detectors, particularly if the first Cherenkov photons can be uniquely timestamped. To maximize the utility of Cherenkov signatures, we employed an optical photon counting detector concept based on a thick, semi-monolithic BGO crystal coupled to a silicon photomultiplier (SiPM) array that provides digital photon timestamps from each SiPM channel. We characterized a prototype detector to demonstrate this concept and explored the use of rich spatiotemporal information of photon transport kinetics.Approach. The detector was built using a 42.68 × 2 × 20 mm³BGO crystal and a 16 × 1 array of 2 × 2 mm³SiPMs with a 2.68 mm pitch. A 16-channel low-noise high-frequency signal processing chain with fast comparators generated digital photon signals, which were recorded using waveform digitizers. Three-dimensional (3D) position calibration and first photon delay distribution (FPDD) construction provided the basis for data-driven methods to improve time resolution and estimate the probability of Cherenkov detection for each event.Main results. With a sufficient number of SiPM channels and 1.8 ns signal shaping, approximately 77% of events were uniquely timestamped with the first photon. FPDD clearly captured photon arrival properties, parameterized with the Cherenkov and scintillation contributions. A coincidence time resolution with a reference detector of 172 ps full width at half maximum was achieved by FPDD-based correction of 3D position dependence. Parameters investigated for Cherenkov detection probability estimation showed consistent correlation with time resolution.Significance. The results demonstrated the feasibility of a photon counting BGO detector for TOF-PET with both promising timing and positioning performance. The abundance of photon information provides a strong basis for further performance gains through data-driven Cherenkov identification and advanced event-by-event corrections.