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:人工智能正在进入医学成像,通常侧重于图像重建或增强分析再造图像。然而,在整个处理链中,从检测信号到计算数据,优化了整个处理链的优化,从而实现了显著的改进。因此,我们展示了一种通过利用残余物理概念进行强化学习的检测优化方法。在我们的工作中,我们改进了正对离子排放成像仪(PET)的巧合时间分辨率(CTR ) 。PET 能够通过检测反光探测器来成像代谢过程。当前研究利用了光共享探测器,即闪烁光分布到计算数据,通过一系列读出渠道进行数字化。虽然这些探测器展示了极好的性能参数,例如空间分辨率,为飞行时间提取精确的定时信息(TF),但随着时间的恶化,我们提高了时间。常规修正方法主要依靠分析模型的配置,理论上能够覆盖所有时间基流效应,例如由信号运行时间或物理效果造成的。然而,利用了50号光相光线光线光线光线光线,通过一系列读取数据渠道进行数字化数字化。这些探测器使用了一个精确的精确的温度测算,因此,因此,我们使用的C- 利用了一种精确变压的研磨变动的研测过程的C-,从而找到了的研磨变动的研测,因此使用了一个精确的C-级的研磨变动的C-级的研磨变动的C-,因此使用了一个精确的研制的研制的C- 。