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]DPA-714 data, where we integrate perpendicular pre-trained SGMs to eliminate slice inconsistency issues.

翻译：利用预训练的分数生成模型进行医学图像重建，相较于其他现有的最先进深度学习重建方法具有优势，包括对不同扫描仪设置的更强适应性和更先进的图像分布建模能力。基于分数生成模型的重建方法最近已应用于模拟正电子发射断层扫描数据集，显示出相对于最先进方法对分布外病灶对比度恢复的改进。然而，现有基于分数生成模型的PET数据重建方法存在重建速度慢、超参数调优负担重以及三维重建中的切片不一致效应等问题。本研究提出一种实用的全三维重建方法，通过将分数生成模型反向扩散过程的似然与最大似然期望最大化算法的当前迭代值相匹配，从而加速重建过程并减少关键超参数数量。以模拟$[^{18}$F]DPA-714数据集的低计数重建为例，我们证明该方法在保持或提升现有最先进分数生成模型PET重建的归一化均方根误差和结构相似性指标的同时，显著缩短了重建时间并降低了对超参数调优的需求。我们将该方法与最先进的监督式及传统重建算法进行了对比评估。最后，我们首次实现了基于分数生成模型的真实三维PET数据重建（特别是$[^{18}$F]DPA-714数据），通过整合垂直预训练的分数生成模型消除了切片不一致问题。