Background Current clinical positron emission tomography (PET) systems utilize detectors where the scintillator typically contains single elements of 3–6‐mm width and about 20‐mm height. While providing good time‐of‐flight performance, this design limits the spatial resolution and causes radial astigmatism as the depth‐of‐interaction (DOI) remains unknown. Purpose We propose an alternative, aiming to combine the advantages of current detectors with the DOI capabilities shown for monolithic concepts, based on semi‐monolithic scintillators (slabs). Here, the optical photons spread along one dimension enabling DOI‐encoding with a still small readout area beneficial for timing performance. Methods An array of eight monolithic LYSO slabs of dimensions 3.9 × 32 × 19 mm3 was read out by a 64‐channel photosensor containing digital SiPMs (DPC3200‐22‐44, Philips Digital Photon Counting). The position estimation in the detector's monolithic and DOI direction was based on a calibration with a fan beam collimator and the machine learning technique gradient tree boosting (GTB). Results We achieved a positioning performance in terms of mean absolute error (MAE) of 1.44 mm for the monolithic direction and 2.12 mm for DOI considering a wide energy window of 300–700 keV. The energy resolution was determined to be 11.3%, applying a positional‐dependent energy calibration. We established both an analytical and machine‐learning‐based timing calibration approach and applied them for a first‐photon trigger. The analytical timing calibration corrects for electronic and optical time skews leading to 240 ps coincidence resolving time (CRT) for a pair of slab‐detectors. The CRT was significantly improved by utilizing GTB to predict the time difference based on specific training data and applied on top of the analytical calibration. We achieved 209 ps for the wide energy window and 198 ps for a narrow selection around the photopeak (411–561 keV). To maintain the detector's sensitivity, no filters were applied to the data during processing. Conclusion Overall, the semi‐monolithic detector provides attractive performance characteristics. Especially, a good CRT can be achieved while introducing DOI capabilities to the detector, making the concept suitable for clinical PET scanners.

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Objective. Positron emission tomography (PET) detectors providing attractive coincidence time resolutions (CTRs) offer time-of-flight information, resulting in an improved signal-to-noise ratio of the PET image. In applications with photosensor arrays that employ timestampers for individual channels, timestamps typically are not time synchronized, introducing time skews due to different signal pathways. The scintillator topology and transportation of the scintillation light might provoke further skews. If not accounted for these effects, the achievable CTR deteriorates. We studied a convex timing calibration based on a matrix equation. In this work, we extended the calibration concept to arbitrary structures targeting different aspects of the time skews and focusing on optimizing the CTR performance for detector characterization. The radiation source distribution, the stability of the estimations, and the energy dependence of calibration data are subject to the analysis. Approach. A coincidence setup, equipped with a semi-monolithic detector comprising 8 LYSO slabs, each 3.9mm×31.9mm×19.0mm, and a one-to-one coupled detector with 8×8 LYSO segments of 3.9mm×3.9mm×19.0mm volume is used. Both scintillators utilize a dSiPM (DPC3200-22-44, Philips Digital Photon Counting) operated in first photon trigger. The calibration was also conducted with solely one-to-one coupled detectors and extrapolated for a slab-only setup. Main results. All analyzed hyperparameters show a strong influence on the calibration. Using multiple radiation positions improved the skew estimation. The statistical significance of the calibration dataset and the utilized energy window was of great importance. Compared to a one-to-one coupled detector pair achieving CTRs of 224 ps the slab detector configuration reached CTRs down to 222 ps, demonstrating that slabs can compete with a clinically used segmented detector design. Significance. This is the first work that systematically studies the influence of hyperparameters on skew estimation and proposes an extension to arbitrary calibration structures (e.g., scintillator volumes) of a known calibration technique.

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Artificial intelligence is finding its way into medical imaging, usually focusing on image reconstruction or enhancing analytical reconstructed images. However, optimizations along the complete processing chain, from detecting signals to computing data, enable significant improvements. Adaptions of the data acquisition and signal processing are potentially easier to translate into clinical applications, as no patient data are involved in the training. At the same time, the robustness of the involved algorithms and their influence on the final image might be easier to demonstrate. Thus, we present an approach toward detector optimization using boosted learning by exploiting the concept of residual physics. In our work, we improve the coincidence time resolution (CTR) of positron emission tomography (PET) detectors. PET enables imaging of metabolic processes by detecting γ-photons with scintillation detectors. Current research exploits light-sharing detectors, where the scintillation light is distributed over and digitized by an array of readout channels. While these detectors demonstrate excellent performance parameters, e.g., regarding spatial resolution, extracting precise timing information for time-of-flight (TOF) becomes more challenging due to deteriorating effects called time skews. Conventional correction methods mainly rely on analytical formulations, theoretically capable of covering all time skew effects, e.g., caused by signal runtimes or physical effects. However, additional effects are involved for lightsharing detectors, so finding suitable analytical formulations can become arbitrarily complicated. The residual physicsbased strategy uses gradient tree boosting (GTB) and a