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Abstract #4686

Learned Subspace Model Enables Ultrafast Neonatal Brain MR Imaging

Ziwen Ke1, Yue Guan1, Yudu Li2, Yunpeng Zhang1, Tianyao Wang3, Ziyu Meng1, Ting Zhao1, Yujie Hu1, Ruihao Liu1, Huixiang Zhuang1, Zhi-Pei Liang2, and Yao Li1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Department Radiology, The Fifth People’s Hospital of Shanghai, Fudan University, Shanghai, China


Fast imaging is essential in neonatal brain MRI. Deep learning-based methods can provide high acceleration rates but their performance is instable when limited training data are available. Subspace model-based approach could reduce the dimensionality of imaging and improve reconstruction stability. This work presents a novel method to integrate neonate-specific subspace model and model-driven deep learning, making stable and ultrafast neonatal MR imaging possible. The feasibility and potential of the proposed method have been demonstrated using in vivo data from four medical centers, producing very encouraging results. With further development, the proposed method may provide an effective tool for neonatal imaging.

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