Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. However, there exists complementary information among multi-modal images. The complementary information can contribute to image reconstruction. In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion. MC-Diffusion learns the joint probability distribution of PET and MRI for utilizing complementary information. We conducted a series of contrast experiments about LPLS, Joint ISAT-net and MC-Diffusion by the ADNI dataset. The results underscore the qualitative and quantitative improvements achieved by MC-Diffusion, surpassing the state-of-the-art method.

翻译：正电子发射断层扫描与磁共振成像（PET-MRI）系统能够获取功能性与解剖学扫描。PET存在信噪比低的问题，而MRI的k空间数据采集过程较为耗时。本研究旨在加速MRI并提升PET图像质量。传统方法对PET-MRI系统中的每种模态进行独立重建。然而，多模态图像之间存在互补信息，这些互补信息有助于图像重建。本文提出一种新颖的PET-MRI联合重建模型——基于互一致性驱动的扩散模型（MC-Diffusion）。MC-Diffusion通过学习PET与MRI的联合概率分布来利用互补信息。我们基于ADNI数据集对LPLS、Joint ISAT-net及MC-Diffusion进行了系列对比实验。结果表明，MC-Diffusion在定性与定量指标上均取得显著提升，超越了现有最先进方法。