RAKI informed unrolled iterations for robust simultaneous multi-slice reconstruction on new datasets

Motivation: To improve reconstruction quality of Simultaneous Multi-Slice (SMS) imagingGoal(s): To develop a deep learning reconstruction method without sacrificing image detail and fidelity, even in data-scarce scenarios. Approach: Our approach involves a novel integration of subject-specific RAKI and a Variational Network (VarNet) within an unrolled iteration framework, testing three different guidance strategies to improve reconstruction quality. Results: The RAKI-VarNet In-Iteration Parallel method yielded the most promising results, showing a reduction in noise and artifacts while maintaining robustness on both seen and unseen data sets, including challenging EPI data. Impact: This technique presents a robust solution for high-fidelity SMS MRI reconstruction with improved generalizability and detail preservation.