Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI
  Co-Reconstruction
Taofeng Xie Zhuo-Xu Cui Chen Luo Huayu Wang Congcong Liu Yuanzhi Zhang Xuemei Wang Yanjie Zhu Qiyu Jin Guoqing Chen Yihang Zhou Dong Liang Haifeng Wang
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.