标题： 基于K空间扩散模型的多层同时成像磁共振重建方法 显示英文标题

标题： K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

摘要： 同时多层(SMS)是一种磁共振成像(MRI)技术，它使用多带射频脉冲同时激发多个层面，以减少扫描时间。 然而，由于其数据结构的可变性和采集的难度，将SMS数据作为训练数据集成到深度学习框架中具有挑战性。本研究提出了一种新的k空间扩散模型用于SMS重建，该模型在训练过程中不使用SMS数据。 相反，它在采样过程中结合了Slice GRAPPA，以从不同的采集模式中重建SMS数据。我们的结果表明，这种方法优于传统的SMS重建方法，并且可以在没有平面内混叠的情况下实现更高的加速因子。 显示英文摘要

摘要： Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This study proposed a novel k-space diffusion model of SMS reconstruction that does not utilize SMS data for training. Instead, it incorporates Slice GRAPPA during the sampling process to reconstruct SMS data from different acquisition modes.Our results demonstrated that this method outperforms traditional SMS reconstruction methods and can achieve higher acceleration factors without in-plane aliasing.