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Abstract #4747

Non-smooth Convex Optimization for O-Space Reconstruction

Jing Cheng1, Haifeng Wang1, Yuchou Chang2, and Dong Liang1,3

1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institude of Advanced Technoleoy,Chinese Academy of Sciences, Shenzhen, China, 2Department of Computer Science and Engineering Technology, University of Houston - Downtown, Houston, TX, United States, 3Research Center for Medical AI, Shenzhen Institude of Advanced Technoleoy, Chinese Academy of Sciences, Shenzhen, China

Non-linear spatial encoding magnetic (SEM) fields can accelerate data acquisitions and improve the image quality. O-Space imaging generates a radially varying SEM field for spatial encoding in order to achieve more efficient encoding. In this work, we introduce and evaluate a novel primal dual algorithm which can handle the inverse problems of non-smooth convex optimization with non-linear forward operators to reconstruct O-Space images from undersampled data. The experimental results on simulated data show that the proposed method can achieve better image quality compared with the existing methods.

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