Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction

• URL: http://arxiv.org/abs/2311.14473v2
• Date: Thu, 11 Jul 2024 00:51:11 GMT
• Title: Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
• Authors: Taofeng Xie, Zhuo-Xu Cui, Chen Luo, Huayu Wang, Congcong Liu, Yuanzhi Zhang, Xuemei Wang, Yanjie Zhu, Guoqing Chen, Dong Liang, Qiyu Jin, Yihang Zhou, Haifeng Wang,
• Abstract summary: The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. We propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion.
• Score: 19.790873500057355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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