Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to the complex signal interactions between and within the excited slices. This study presents a robust SMS MRI reconstruction method using deep generative priors. Starting from Gaussian noise, we leverage denoising diffusion probabilistic models (DDPM) to gradually recover the individual slices through reverse diffusion iterations while imposing data consistency from the measured k-space under readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without special adjustments for SMS tasks. Additionally, our method integrates a low-frequency enhancement (LFE) module to address a practical issue that SMS-accelerated fast spin echo (FSE) and echo-planar imaging (EPI) sequences cannot easily embed autocalibration signals. Extensive experiments demonstrate that our approach consistently outperforms existing methods and generalizes well to unseen datasets. The code is available at https://github.com/Solor-pikachu/ROGER after the review process.

本研究针对同时多切片（SMS）成像中的复杂信号交互问题，提出了一种基于深度生成先验的鲁棒性重建方法。该方法利用去噪扩散概率模型（DDPM）逐步恢复各个切片，并在重构过程中保持数据的一致性。实验结果表明，该方法在性能上超越现有技术，并能有效推广至未见数据集。

基于深度生成先验的鲁棒性同时多切片MRI重建