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Abstract #3842

A Machine Learning Based Biomechanical Model for Real-time MR-guided Neuro-intervention

Suhao Qiu1, Alexa Singer1,2, Changxin Lai1, Blanca Zufiria1,2, Danni Wang1, Yao Li1, Bomin Sun3, Yiping Du1, Zhi-Pei Liang4, and Yuan Feng1

1Biomedeical Engineering, Shanghai Jiaotong University, Shanghai, China, 2KTH Royal Institute of Technology, Stockholm, Sweden, 3Functional Neurosurgery, Shanghai Jiaotong University, Shanghai, China, 4Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Magnetic resonance (MR) guided neuro interventions could be combined with robotic assisted manipulation to achieve optimal performance. Patient specific model constructed from MR images of the brain could have the best biophysical fidelity but suffers from high computational cost. For real-time applications, we proposed to construct an Artificial Neural Network (ANN) based on the training from computational outputs of Finite element Analysis (FEA). Results demonstrate the ability to achieve accurate predictions given by a mean square error (MSE) of 0.0338 mm2 within 10ms.

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