Dynamic PET enables the quantitative estimation of physiology-related parameters and is widely utilized in research and increasingly adopted in clinical settings. Parametric imaging in dynamic PET requires kinetic modeling to estimate voxel-wise physiological parameters based on specific kinetic models. However, parametric images estimated through kinetic model fitting often suffer from low image quality due to the inherently ill-posed nature of the fitting process and the limited counts resulting from non-continuous data acquisition across multiple bed positions in whole-body PET. In this work, we proposed a diffusion model-based kinetic modeling framework for parametric image estimation, using the Patlak model as an example. The score function of the diffusion model was pre-trained on static total-body PET images and served as a prior for both Patlak slope and intercept images by leveraging their patch-wise similarity. During inference, the kinetic model was incorporated as a data-consistency constraint to guide the parametric image estimation. The proposed framework was evaluated on total-body dynamic PET datasets with different dose levels, demonstrating the feasibility and promising performance of the proposed framework in improving parametric image quality.

翻译：动态正电子发射断层扫描（PET）能够对生理相关参数进行定量估计，广泛应用于研究领域，并日益被临床采用。动态PET中的参数成像需要通过动力学建模，基于特定动力学模型估计体素级的生理参数。然而，通过动力学模型拟合估计的参数图像往往存在图像质量低下的问题，这源于拟合过程本身的不适定性，以及全身PET中多床位非连续数据采集导致的有限计数。在本研究中，我们提出了一种基于扩散模型的动力学建模框架用于参数图像估计，并以帕特拉克模型为例。扩散模型的得分函数在静态全身PET图像上进行预训练，并通过利用帕特拉克斜率与截距图像的块级相似性，作为两者的先验。在推断过程中，动力学模型被整合为数据一致性约束，以指导参数图像的估计。所提出的框架在不同剂量水平的全身动态PET数据集上进行了评估，结果证明了该框架在提升参数图像质量方面的可行性与良好性能。