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Abstract #4478

Self-navigation Liver Respiratory Motion Correction Based on Deep Learning

Yu Wang1, Haikun Qi2, Guanhua Wang1, Yuze Li1, and Huijun Chen1

1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Correction of respiratory motion with 100% acquisition efficiency is of great significance for clinical abdominal imaging. In this study, we propose a novel self-navigation liver respiratory motion correction method for 3D radial sampling. This new approach is based on the fact that radial acquisition enables oversampled k-space center to extract motion-state signal and neural network can be used for data dimensionality reduction. Both regular and irregular hepatic breathing experiments were conducted and the proposed method has shown similar reconstruction image quality with bellow.

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