Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors

TL;DR

ROGER is a novel MRI reconstruction method that employs slice-wise learned diffusion priors, enabling robust and high-quality simultaneous multislice imaging by leveraging deep generative models and data consistency techniques.

Contribution

This work introduces ROGER, a diffusion-based MRI reconstruction framework that trains on single-slice images and effectively handles SMS imaging without modifications.

Findings

Outperforms existing SMS reconstruction methods in experiments.

Enhances anatomical and functional MRI imaging quality.

Demonstrates strong generalization to out-of-distribution data.

Abstract

Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited slices. In this study, we introduce ROGER, a robust SMS MRI reconstruction method based on deep generative priors. Utilizing denoising diffusion probabilistic models (DDPM), ROGER begins with Gaussian noise and gradually recovers individual slices through reverse diffusion iterations while enforcing data consistency from measured k-space data within the readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without requiring modifications for SMS tasks. Additionally, our method incorporates a low-frequency enhancement (LFE) module to address the practical issue that SMS-accelerated fast spin echo (FSE) and echo planar imaging (EPI) sequences cannot easily embed fully-sampled autocalibration signals. Extensive experiments on both retrospectively and prospectively accelerated datasets demonstrate that ROGER consistently outperforms existing methods, enhancing both anatomical and functional imaging with strong out-of-distribution generalization. The source code and sample data for ROGER are available at https://github.com/Solor-pikachu/ROGER.