无需训练数据，用扩散模型实现高加速的多层同时成像重建。
K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging
- 在采样阶段引入Slice GRAPPA，不依赖SMS训练数据。
- 可支持更高加速因子，且无平面内伪影。
- 适合需要快速扫描且数据难获取的MRI场景。
同时多层（Simultaneous Multi-Slice, SMS）是磁共振成像（MRI）中通过多带射频脉冲同时激发多个层面以缩短扫描时间的技术。然而，由于其数据结构多样且采集困难，难以将SMS数据纳入深度学习框架进行训练。本文提出一种基于k空间扩散模型的SMS重建方法，不使用SMS数据进行训练，而是在采样过程中结合Slice GRAPPA，从不同采集模式中重建SMS数据。实验结果表明，该方法优于传统SMS重建方法，可在不产生平面内伪影的情况下实现更高的加速因子。
原文摘要 · Abstract (English)
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.
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