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Abstract #3626

A Recurrent Neural Network (RNN) based reconstruction of extremely undersampled neuro-interventional MRI

Ruiyang Zhao1, Tao Wang2, Kang Yan1, Chengcheng Zhang3, Zhipei Liang4, Yiping Du1, Dianyou Li3, Bomin Sun3, and Yuan Feng1
1Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China, 2Functional Neurosurgery,Ruijin Hospital affiliated to Shanghai Jiao Tong University, Shanghai, China, 3Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, CHINA, Shanghai, China, 4Beckman Institute for Advanced Science & Technology, Department of Electrical & Computer Engineeringļ¼ŒUniversity of Illinois at Urbana-Champaign, Urbana-Champaign, IL, United States

Real ā€“time MR image-guided neurosurgery could greatly improve the surgery accuracy and outcome. However, real-time guidance requires highly accelerated imaging. In this study, we proposed a Convolutional Long Short-term Memory (Conv-LSTM) based U-net to reconstruct consecutive image frames with golden-angle sampling. The Conv-LSTM based architecture was developed to explore time coherence information. Training and test datasets were generated from MR images of patients treated with Deep Brain Stimulation (DBS). Results showed that our model could achieve an acceleration rate ~80x, which provided great potentials for application in MR-guided interventional therapy.

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