Exploiting network optimization stability for enhanced PET image denoising using deep image prior

Objective. Positron emission tomography (PET) is affected by statistical noise due to constraints on tracer dose and scan duration, impacting both diagnostic performance and quantitative accuracy. While deep learning-based PET denoising methods have been used to improve image quality, they may introduce over-smoothing, which can obscure critical structural details and compromise quantitative accuracy. We propose a method for making a deep learning solution more reliable and apply it to the conditional deep image prior (DIP).Approach. We introduce the idea ofstability informationin the optimization process of conditional DIP, enabling the identification of unstable regions within the network's optimization trajectory. Our method incorporates a stability map, which is derived from multiple intermediate outputs of a moderate neural network at different optimization steps. The final denoised PET image is then obtained by computing a linear combination of the DIP output and the original reconstructed PET image, weighted by the stability map.Main results. We employed eight high-resolution brain PET datasets for comparison. Our method effectively reduces background noise while preserving small structure details in brain [¹⁸F]FDG PET images. Comparative analysis demonstrated that our approach outperformed existing methods in terms of peak-to-valley ratio and background noise suppression across various low-dose levels. Additionally, region-of-interest analysis confirmed that the proposed method maintains quantitative accuracy without introducing under- or over-estimation. Furthermore, we applied our method to full-dose PET data to assess its impact on image quality. The results revealed that the proposed method significantly reduced background noise while preserving the peak-to-valley ratio at a level comparable to that of unfiltered full-dose PET images.Significance. The proposed method introduces a robust approach to deep learning-based PET denoising, enhancing its reliability and preserving quantitative accuracy. Furthermore, this strategy can potentially advance performance in high-sensitivity PET scanners and surpass the limit of image quality inherent to PET scanners.