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 Engineering，University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, United States

Real –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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