Estimation of intra-operative brain shift based on constrained Kalman filter |
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| Affiliation: | 1. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;2. The Center of Excellence in Control and Robotics, Amirkabir University of Technology, Tehran, Iran;1. College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, PR China;2. School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK;3. Signal Processing and Algorithms Group, School of Engineering, Manchester Metropolitan University, All Saints Building, All Saints, Manchester M15 6BH, UK;4. College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi 030024, PR China |
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| Abstract: | In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method. |
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| Keywords: | Brain shift Constrained Kalman filter Neuronavigation systems Estimation theory |
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