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Abstract #0248

Highly Undersampled Kooshball Reconstruction with Low-rank Modeling and Sparsity Constraints for High-resolution T1 Mapping

Haikun Qi1, Huiyu Qiao1, Aiqi Sun1, Shuo Chen1, Xihai Zhao1, Rui Li1, Chun Yuan1,2, and Huijun Chen1

1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Department of Radiology, University of Washington, Seattle, WA, United States

Highly undersampled 3D radial is very useful for 3D imaging acceleration, and compressed sensing and low-rank can be used for reconstruction of the undersampled kooshball data. In this study, we propose a novel reconstruction method for fast 3D T1 mapping of carotid artery using 3D radial sampling. The reconstruction method is based on low-rank modeling with parallel imaging and sparsity constraints, and is potential to improve the accuracy and precision of T1 estimation. The aim of this study is to evaluate the effectiveness of the proposed method using phantom and in vivo imaging experiments on volunteers and carotid atherosclerosis patients.

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