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.

该研究提出了一种新的 PET-MRI 联合重建模型，名为 MC-Diffusion，利用互补信息学习 PET 和 MRI 的联合概率分布，加速 MRI 并提高 PET 图像质量，实验证明 MC-Diffusion 在 ADNI 数据集上取得了显著的定性和定量改进，超过了现有方法。