Abstract #3710

A Faithful Deep Sensitivity Estimation Makes High-quality MRI Reconstruction

Zi Wang¹, Haoming Fang¹, Chen Qian¹, Boxuan Shi¹, Lijun Bao¹, Liuhong Zhu², Jianjun Zhou², Wenping Wei³, Jianzhong Lin⁴, Di Guo⁵, and Xiaobo Qu¹

¹Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, ²Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China, ³Department of Radiology, Xiamen University, Xiamen, China, ⁴Department of Radiology, Zhongshan Hospital affiliated to Xiamen University, Xiamen, China, ⁵School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceRecent deep learning is superior in providing high-quality images and ultra-fast reconstructions in accelerated magnetic resonance imaging (MRI). Faithful coil sensitivity estimation is vital for MRI reconstruction. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network (JDSI). During the image artifacts removal, it gradually provides more faithful sensitivity maps, leading to greatly improved image reconstructions. Results on in vivo datasets and radiologist reader study demonstrate that, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the accelerated factor is high. Besides, JDSI also owns nice robustness to abnormal subjects.

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