Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction

作者：George Webber, Yuya Mizuno, Oliver Howes, Alexander Hammers, Andrew P. King, Andrew J. Reader · 发表于：IEEE Transactions on Medical Imaging · 年份：2025 · DOI：10.1109/tmi.2025.3576483 · 被引用次数：6 · 研究领域：Medical Imaging Techniques and Applications、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing methods for SGM-based reconstruction from PET data suffer from slow reconstruction, burdensome hyperparameter tuning and slice inconsistency effects (in 3D). In this work, we propose a practical methodology for fully 3D reconstruction that accelerates reconstruction and reduces the number of critical hyperparameters by matching the likelihood of an SGM's reverse diffusion process to a current iterate of the maximum-likelihood expectation maximization algorithm. Using the example of low-count reconstruction from simulated [ ${}^{{18}}$ F]DPA-714 datasets, we show our methodology can match or improve on the NRMSE and SSIM of existing state-of-the-art SGM-based PET reconstruction while reducing reconstruction time and the need for hyperparameter tuning. We evaluate our methodology against state-of-the-art supervised and conventional reconstruction algorithms. Finally, we demonstrate a first-ever implementation of SGM-based reconstruction for real 3D PET data, specifically [ ${}^{{18}}$ F]...