Abstract #3842

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

Suhao Qiu¹, Alexa Singer^1,2, Changxin Lai¹, Blanca Zufiria^1,2, 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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