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.
@article{arxiv.2311.14473,
title = {Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction},
author = {Taofeng Xie and Zhuo-Xu Cui and Chen Luo and Huayu Wang and Congcong Liu and Yuanzhi Zhang and Xuemei Wang and Yanjie Zhu and Guoqing Chen and Dong Liang and Qiyu Jin and Yihang Zhou and Haifeng Wang},
journal= {arXiv preprint arXiv:2311.14473},
year = {2024}
}