برآورد تغییر مغز حین عمل بر اساس فیلتر کالمن محدود
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|53095||2015||7 صفحه PDF||سفارش دهید||5216 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : ISA Transactions, Volume 55, March 2015, Pages 260–266
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.