Dynamic positron emission tomography image reconstruction using spatiotemporal kernel method with deep image prior.

Background: The inherently ill-posed nature of tomographic reconstruction, combined with the low photon counts in short acquisition frames, poses significant challenges for achieving high-quality dynamic positron emission tomography (PET) images. Recent studies have shown that deep learning methods, particularly the deep image prior (DIP) framework, can effectively enhance PET image reconstruction without requiring external training data. This study aimed to develop a novel DIP-based reconstruction algorithm integrated with a spatiotemporal kernel and to validate its effectiveness in improving the quality of reconstructed dynamic PET images. Methods: We constructed a novel dynamic PET reconstruction algorithm that integrates a spatiotemporal kernel with a DIP framework. Specifically, the reconstruction objective function was formulated as a constrained optimization problem. This problem was solved using the alternating direction method of multipliers (ADMM), alternating between image reconstruction and network parameter updates. Additionally, the algorithm supports list-mode reconstruction, enabling full 3-dimensional (3D) imaging and scalability to large PET systems. Results: Simulation and preclinical studies demonstrated that the proposed method substantially improved image quality compared to conventional methods such as maximum-likelihood expectation maximization (MLEM) with Gaussian post-filtering, kernel expectation maximization (KEM), spatiotemporal kernel expectation maximization (STKEM), as well as deep learning-based methods including DIPRecon and NeuralKEM. It achieved higher signal-to-noise ratio (SNR) and structural similarity index (SSIM), demonstrated good stability during the iterative reconstruction process, and attained a comparable level of contrast recovery coefficient (CRC), while effectively mitigating noise amplification in later iterations. Importantly, the method also demonstrated robust performance under low-count conditions, preserving image quality when conventional methods degrade significantly. Conclusions: By leveraging intrinsic spatiotemporal information, the proposed method improves dynamic PET reconstruction accuracy without external priors. Its modular design enables seamless integration into existing workflows and adapts to diverse PET acquisition protocols. These results underscore its potential for clinical and preclinical dynamic PET imaging, particularly for low-dose or high-resolution scenarios.