Predicting time-of-flight with Cerenkov light in BGO: a three-stage network approach with multiple timing kernels prior.

In the quest for enhanced image quality in positron emission tomography (PET) reconstruction, the introduction of time-of-flight (TOF) constraints in TOF-PET reconstruction offers superior signal-to-noise ratio. By employing BGO detectors capable of simultaneously emitting prompt Cerenkov light and scintillation light, this approach combines the high time resolution of prompt photons with the high energy resolution of scintillation light, thereby presenting a promising avenue for acquiring more precise TOF information.In Stage One, we train a raw method capable of predicting TOF information based on coincidence waveform pairs. In Stage Two, the data is categorized into 25 classes based on signal rise time, and the pre-trained raw method is utilized to obtain TOF kernels for each of the 25 classes, thereby generating prior knowledge. Within Stage Three, our proposed deep learning (DL) module, combined with a bias fine-tuning module, utilizes the kernel prior to provide bias compensation values for the data, thereby refining the first-stage outputs and obtaining more accurate TOF predictions.The three-stage network built upon the LED method resulted in improvements of 11.7 ps and 41.8 ps for full width at half maximum (FWHM) and full width at tenth maximum (FWTM), respectively. Optimal performance was achieved with FWHM of 128.2 ps and FWTM of 286.6 ps when CNN and Transformer were utilized in Stages One and Three, respectively. Further enhancements of 2.3 ps and 3.5 ps for FWHM and FWTM were attained through data augmentation methods.This study employs neural networks to compensate for the timing delays in mixed (Cerenkov and scintillation photons) signals, combining multiple timing kernels as prior knowledge with DL models. This integration yields optimal predictive performance, offering a superior solution for TOF-PET research utilizing Cerenkov signals.