Abstract #4662

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

Lifeng Mei¹, Kexin Yang¹, Yi Li¹, Shoujin Huang¹, Jingyu Li¹, Jingzhe Liu², Hua Guo³, Bing Wu⁴, Yuhui Xiong⁴, Lingyan Zhang⁵, and Mengye Lyu¹

¹Shenzhen Technology University, Shenzhen, China, ²Department of Radiology, The First Hospital of Tsinghua University, Beijing, China, ³Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ⁴GE HealthCare MR Research, Beijing, China, ⁵Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China

Synopsis

Keywords: AI/ML Image Reconstruction, Data Processing, SMS,VarNet

Motivation: To improve reconstruction quality of Simultaneous Multi-Slice (SMS) imaging

Goal(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.

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