SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
This work addresses MRI reconstruction for vessel wall imaging, which is important for medical diagnosis, but appears incremental as it adapts existing diffusion models to multi-coil data.
The authors tackled MRI reconstruction for vessel wall imaging by developing SPIRiT-Diffusion, a diffusion model that incorporates multi-coil acquisition characteristics and self-consistency priors, achieving superior reconstruction results on a joint intracranial and carotid vessel wall imaging dataset.
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve superior reconstruction results on the joint Intracranial and Carotid Vessel Wall imaging dataset.