Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We also show how our pseudo-PET images may be exploited as a generative prior for single-subject PET image reconstruction. Firstly, we perform deep-learned deformable registration of multi-subject magnetic resonance (MR) images paired to multi-subject PET images. We then use the anatomically-learned deformation fields to transform multiple PET images to the same reference space, before averaging random subsets of the transformed multi-subject data to form a large number of varying pseudo-PET images. We observe that using MR information for registration imbues the resulting pseudo-PET images with improved anatomical detail compared to the originals. We consider applications to PET image reconstruction, by generating pseudo-PET images in the same space as the intended single-subject reconstruction and using them as training data for a diffusion model-based reconstruction method. We show visual improvement and reduced background noise in our 2D reconstructions as compared to OSEM, MAP-EM and an existing state-of-the-art diffusion model-based approach. Our method shows the potential for utilising highly subject-specific prior information within a generative reconstruction framework. Future work may compare the benefits of our approach to explicitly MR-guided reconstruction methodologies.

翻译：获取大规模高质量医学图像数据集虽困难但对众多深度学习应用至关重要。在正电子发射断层扫描（PET）中，重建图像质量受限于固有的泊松噪声。本文提出一种合成多样化、高真实度且具有改进信噪比的伪PET图像的新方法，并展示如何将此类伪PET图像作为生成先验用于单主体PET图像重建。首先，我们对多主体磁共振（MR）图像与多主体PET图像进行深度学习驱动的可变形配准，随后利用解剖学驱动的形变场将多幅PET图像变换至同一参考空间，再对变换后的多主体数据随机子集进行平均以生成大量多样化的伪PET图像。我们观察到，相较于原始图像，利用MR信息进行配准能使生成的伪PET图像获得更优的解剖细节。通过在与目标单主体重建相同的空间中生成伪PET图像，并将其作为基于扩散模型的重建方法的训练数据，我们探讨了该方法在PET图像重建中的应用。与OSEM、MAP-EM及现有最先进的基于扩散模型的方法相比，我们的二维重建结果在视觉质量上有所提升且背景噪声降低。本方法展现了在生成式重建框架中利用高度主体特异性先验信息的潜力。未来工作可比较本方法与显式MR引导重建方法的优势。