论文标题
精神扩散：船只墙成像的基于精神驱动的分数生成建模
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
论文作者
论文摘要
扩散模型是图像生成中最先进的方法，已成功应用于MRI重建。但是，现有方法不考虑MRI数据多型元素获取的特征。因此，我们根据精神迭代重建算法提供了一种新的扩散模型，称为精神扩散。具体而言，精神扩散是通过分数匹配来表征线圈图像的先前分布，并表征了基于自一致性的线圈之间的k空间冗余先验。使用足够的先前约束，我们在颅内和颈动脉壁成像数据集上实现了优越的重建结果。
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.
