Diffusion models are a leading method for image generation and have been
successfully applied in magnetic resonance imaging (MRI) reconstruction.
Current diffusion-based reconstruction methods rely on coil sensitivity maps
(CSM) to reconstruct multi-coil data. However, it is difficult to accurately
estimate CSMs in practice use, resulting in degradation of the reconstruction
quality. To address this issue, we propose a self-consistency-driven diffusion
model inspired by the iterative self-consistent parallel imaging (SPIRiT),
namely SPIRiT-Diffusion. Specifically, the iterative solver of the
self-consistent term in SPIRiT is utilized to design a novel stochastic
differential equation (SDE) for diffusion process. Then k-space data
can be interpolated directly during the reverse diffusion process, instead of
using CSM to separate and combine individual coil images. This method indicates
that the optimization model can be used to design SDE in diffusion models,
driving the diffusion process strongly conforming with the physics involved in
the optimization model, dubbed model-driven diffusion. The proposed
SPIRiT-Diffusion method was evaluated on a 3D joint Intracranial and Carotid
Vessel Wall imaging dataset. The results demonstrate that it outperforms the
CSM-based reconstruction methods, and achieves high reconstruction quality at a
high acceleration rate of 10