Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

purposeHigh-quality 4D dynamic PET imaging is often compromised by noise, especially in low-count frames, which limits clinical utility and quantitative accuracy. This study proposes a novel spatiotemporal denoising method (SPRINTER) that integrates anatomical priors, self-supervised adaptive principal component analysis (aPCA), and deep learning to enhance image quality and preserve time activity fidelity without relying on ground-truth data.

methodsThe method combines anatomically guided aPCA-based temporal smoothing with a supervised 3D ResUNet for spatial denoising. The resulting high signal-to-noise ratio, spatially denoised images are iteratively refined using a temporal regularization strategy to enhance contrast. The model was trained using brain scan datasets from 220 participants who were administered one of the following PET/MRI radiotracers:

resultsWe compared SPRINTER with OSEM, spatial-only, temporal-only, and two existing unsupervised denoising approaches, including NLM and Noise2Void. Across both simulation and acquired datasets, SPRINTER significantly outperformed all comparison methods (p < 0.05, ANOVA with Bonferroni multiple comparison corrections). The largest gains were observed for the noisiest data using

conclusionThis study introduces a robust, self-supervised spatiotemporal denoising method for 4D dynamic PET imaging of the brain. SPRINTER spatiotemporal denoising leverages anatomical guidance and temporal dynamics to enhance spatial clarity and temporal consistency. Its radiotracer-agnostic design and rigorous self-supervision make it well-suited for clinical applications where high-quality reference data are unavailable.