Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising

Quantitative ¹⁸F-FDG Patlak $K_{\text{i}}$ images provide physiologically meaningful measures of glucose influx, but they are substantially noisier than SUV images because they are estimated from short-duration dynamic frames. This study aims to improve the quality and quantitative reliability of $K_{\text{i}}$ images by applying a self-supervised deep learning framework for dynamic PET denoising. We propose the Patlak-Guided Self-Supervised Denoising Network (PG-SSDNet), a multi-frame CycleGAN-based model that incorporates a Patlak consistency loss to preserve tracer kinetics across frames. PG-SSDNet was evaluated on 14-frame and 6-frame whole-body ¹⁸F-FDG dynamic PET datasets from 27 patients. Performance was compared with a supervised U-Net, a single-frame CycleGAN, and a multi-frame CycleGAN without Patlak guidance. Metrics included mean normalized residual sum of squares (MNRSS) of Patlak fits, liver normalized standard deviation (NSTD), and lesion signal-to-noise ratio (SNR). PG-SSDNet consistently reduced noise while maintaining kinetic fidelity. It achieved the lowest Patlak fitting errors (MNRSS: 0.98% ± 0.14% for 14 frames, 0.85% ± 0.11% for 6 frames), the lowest liver NSTD values (0.105 ± 0.037 for 14 frames, 0.211 ± 0.107 for 6 frames), and the highest lesion SNR across all methods, while preserving accurate mean $K_{\text{i}}$ values. PG-SSDNet effectively denoises ¹⁸F-FDG dynamic PET data while preserving Patlak linearity, improving both lesion quantification and image quality. Unlike supervised methods, it requires no paired noisy/clean datasets, making it practical for clinical deployment. Future work will extend PG-SSDNet to other tracers with different kinetic properties and to larger multi-center datasets.