Score-Based Generative Models for PET Image Reconstruction

Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their application to Positron Emission Tomography (PET) is still largely unexplored. PET image reconstruction involves a variety of challenges, including Poisson noise with high variance and a wide dynamic range. To address these challenges, we propose several PET-specific adaptations of score-based generative models. The proposed framework is developed for both 2D and 3D PET. In addition, we provide an extension to guided reconstruction using magnetic resonance images. We validate the approach through extensive 2D and 3D in-silico experiments with a model trained on patient-realistic data without lesions, and evaluate on data without lesions as well as out-of-distribution data with lesions. This demonstrates the proposed method’s robustness and significant potential for improved PET reconstruction.

基于分数的生成模型在磁共振成像或计算机断层扫描的医学图像重建任务中已展现出极具前景的结果。然而，它们在正电子发射断层扫描（PET）中的应用在很大程度上仍未被探索。PET图像重建涉及多种挑战，包括高方差的泊松噪声和宽动态范围。为应对这些挑战，我们提出了几种针对PET的基于分数的生成模型的改进方法。所提出的框架适用于二维和三维PET。此外，我们提供了一种使用磁共振图像进行引导重建的扩展。我们通过大量二维和三维的计算机模拟实验对该方法进行验证，实验使用在无病变的患者真实数据上训练的模型，并对无病变数据以及有病变的分布外数据进行评估。这证明了所提出方法的稳健性以及在改进PET重建方面的巨大潜力。