  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.
