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. 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 {\gamma}-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 light-sharing detectors, so finding suitable analytical formulations can become arbitrarily complicated. The residual physics-based strategy uses gradient tree boosting (GTB) and a physics-informed data generation mimicking an actual imaging process by shifting a radiation source. We used clinically relevant detectors with a height of 19 mm, coupled to digital photosensor arrays. All trained models improved the CTR significantly. Using the best model, we achieved CTRs down to 198 ps (185 ps) for energies ranging from 300 keV to 700 keV (450 keV to 550 keV).

翻译：人工智能正逐步应用于医学成像领域，通常侧重于图像重建或增强分析重建图像。然而，从信号检测到数据计算的完整处理链优化能带来显著改进。因此，我们提出一种基于残差物理概念的增强学习探测器优化方法。本研究旨在提升正电子发射断层扫描（PET）探测器的符合时间分辨率（CTR）。PET通过使用闪烁探测器检测γ光子实现对代谢过程的成像。当前研究利用光共享探测器，其闪烁光分布并被读出通道阵列数字化。尽管这些探测器在空间分辨率等性能参数上表现优异，但由于时间偏移效应，提取精确的飞行时间（TOF）信息变得更加困难。传统校正方法主要依赖解析公式，理论上可覆盖所有时间偏移效应（如信号传播时间或物理效应引起的问题），但针对光共享探测器还需考虑额外效应，使得寻找合适的解析公式变得极其复杂。基于残差物理的策略采用梯度提升树（GTB）和物理信息数据生成方法，通过移动辐射源模拟实际成像过程。我们使用了高度为19毫米的临床相关探测器，并耦合至数字光电传感器阵列。所有训练模型均显著提升了CTR。最佳模型在300 keV至700 keV（450 keV至550 keV）能量范围内，实现了低至198 ps（185 ps）的CTR。