Beex, Lars

Bordas, Stéphane

Cotin, Stephane

Deshpande, Saurabh

Sarkica, Anina

English

Open Repository and Bibliography - Luxembourg

14th World Congress on Computational Mechanics (WCCM)ECCOMAS Congress 202019–24 July 2020, Paris, FranceDATA DRIVEN SURGICAL SIMULATIONSSaurabh Deshpande1, S. Bordas2, L. Beex3, S. Cotin4, A.Sˇ. Glumac51 University of Luxembourg, Esch-sur-Alzette, saurabh.deshpande@uni.lu2 University of Luxembourg, Esch-sur-Alzette, stephane.bordas@uni.lu3 University of Luxembourg, Esch-sur-Alzette, lars.beex@uni.lu4 Inria Nancy - Grand Est, Villers-les-Nancy, stephane.cotin@inria.fr5 University of Luxembourg, Esch-sur-Alzette, sarkicanina@gmail.comKey Words: Kalman Filters, hyper elasticity, medical simulationsSurgery is a life saver, but too many incidents may occur due to difficult unmastered techniques. Itis important to train performants and skilled surgeons to be prepared to handle any pathology, socomputer simulation has acquired a great importance in recent years. Finite-Element based Surgicalsimulation, pioneered by S. Cotin [1] has played a dominant role in helping surgeons hone theirskills. The main difficulty in surgical simulation is that the amount of patient-specific data availableon a given patient is limited. Mechanical information, which could lead to the identification ofmechanical models and stiffness parameters is particularly scarce. This lack of data makes choosingmodels and parameters a serious challenge in patient-specific surgical simulation. When simulatingsoft tissue responses during surgery, the mathematical model representation of reality is imprecise,for four main reasons:(1) choice and uncertainties of the model(2) choice and uncertainties of the parameters(3) description of the geometry of the organ(4) description of the boundary conditionsKalman filters were shown to be suitable for surgical and interventional radiology guidance [2] andare helpful in improving the simulation of such uncertain systems. The central point of Kalmanfilters is to estimate the state of a process by combining (noisy) physics-based solutions and (noisy)measurement data, which is acquired on fly [3]. In this work, at first different variants of Kalman fil-ters are implemented for simple mechanical systems to explain their effectiveness for simultaneousstate and parameter estimation. In later part, Unscented Kalman filters are implemented for predict-ing non-linear responses which usually arise in hyper-elastic soft tissue systems. For all the cases,normally distributed synthetic measurement data is created around the ideal solution, which will beeventually replaced with real data. Thus, we set a new effective framework to improve conventionalfinite element solvers by incorporating measurements thereby increasing the simulation accuracy.REFERENCES[1] S. Cotin ; H. Delingette ; N. Ayache Real-time elastic deformations of soft tissues for surgerysimulation in IEEE Transactions on Visualization and Computer Graphics, vol. 5, no. 1, pp.62-73,(1999) 35-45[2] R. Trivisonne, E. Kerrien, S. Cotin Constrained Stochastic State Estimation for 3D ShapeReconstruction of Catheters and Guidewires in Fluoroscopic Images hal-02072386 (2019)35-45[3] Rudolph Emil Kalman. A New Approach to Linear Filtering and Prediction Problem. Trans-actions of the ASME–Journal of Basic Engineering, 82, Series D (1960) 35-45

DATA DRIVEN SURGICAL SIMULATIONS

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DATA DRIVEN SURGICAL SIMULATIONS

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