Unsupervised waveform reshaping for enhanced time-of-flight precision in PET detectors

OBJECTIVE: To enhance the image quality in Time-of-Flight Positron Emission Tomography (TOF-PET) reconstruction by improving the accuracy of Time-of-Flight (TOF) estimation through an unsupervised waveform reshaping method based on Cycle-Consistent Generative Adversarial Networks (CycleGAN). This method aims to transform distorted waveforms acquired from real detector responses into clean, virtual waveforms generated by Geant4 simulations, thereby significantly improving the signal-to-noise ratio (SNR) in PET imaging. APPROACH: We developed a real event signal dataset using a 22 22 Na gamma source and LYSO-SiPM detector pairs, and proposed an unsupervised waveform reshaping method based on CycleGAN. The framework learns to map experimentally acquired waveforms-distorted by noise, electronic delays, and scintillator response broadening-to clean, virtual waveforms generated by Geant4 simulations that model ideal detector responses. By leveraging cycle-consistency constraints, the model performs this transformation without requiring paired or labeled data. The reshaped waveforms retain essential timing characteristics while suppressing distortions, thereby enabling more accurate TOF estimation through standard timing pick-off methods such as Constant Fraction Discrimination (CFD). MAIN RESULTS: After applying CFD timing to the reshaped waveforms, the results show a coincidence time resolution (CTR) improvement of 41 ps (15.1%) over conventional CFD applied to raw signals, and 81 ps (26.0%) over Leading Edge Discrimination (LED). Moreover, the proposed method achieves lower edge bias compared to supervised CNN-based approaches. SIGNIFICANCE: Our unsupervised TOF enhancement framework eliminates the need for large-scale labeled datasets and reduces hardware dependency by leveraging simulated virtual waveforms. The CycleGAN-based architecture enables efficient, generalizable performance across source positions, offering a practical and scalable strategy for advancing high-precision TOF-PET systems.