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Identification of Material Parameters for Thermal Ablation

Explore thermal ablation techniques for lesion treatment in human liver, including RF ablation, with a focus on material properties identification and computational simulations. Discuss challenges, solutions, and future outlook.

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Identification of Material Parameters for Thermal Ablation

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  1. Hanne Tiesler – 1 Hanne TieslerCeVis/MeVis/ZeTeM @ University of Bremen, Germany DFG SPP 1253 Identification of Material Parameters for Thermal Ablation

  2. Hanne Tiesler – 2 Joint work with • Inga Altrogge, CeVis, Bremen University, Germany • Tim Kröger, CeVis, Bremen University, Germany • Heinz-Otto Peitgen, CeVis & MeVis Research, Bremen, Germany • Tobias Preusser, CeVis, Bremen University, Germany • Christoph Büskens, ZETEM, Bremen University, Germany • Matthias Gerdts, University of Birmingham, GB • Patrik Kalmbach, ZETEM, Bremen University, Germany • Dennis Wassel, ZETEM, Bremen University, Germany • MeVis Research, Center for Medical Image Computing, Bremen, Germany • Philippe L. Pereira, University Clinic Tübingen, Germany • D. Schmidt, University Clinic Tübingen, Germany

  3. Hanne Tiesler – 3 Img of lesion Treatment of lesions in the human liver • Transplant „Replace“ the liver • Surgical resection Cut the lesion out • Chemotherapy Kill tumor by cytotoxic drugs • Cryotherapy Kill tumor cells by freezing • Thermal Ablation Kill tumor cells by heat

  4. Hanne Tiesler – 4 Bipolar or Multipolar Treatment-Planning

  5. Hanne Tiesler – 5 Lesion Local Vessels RF-Ablation

  6. Hanne Tiesler – 6 • Minimally invasive + • Small equipment • Widelyused • High potential • High risk of under-ablation − • No estimation of risk • No dose planning • No online monitoring Radio Frequency Ablation

  7. Hanne Tiesler – 7 Is a lesion destructable by ablation? Support the choice of optimum therapy-parameters Must perfusion be stopped? Goals of Numerical Support Must several probes be used? How must the probes be placed? Which power must be applied? How long must power be applied?

  8. Hanne Tiesler – 8 Denoising/Enhancement Segmentation • PDE model/Simulation • Electric potential • Heat distribution Image based computing pipeline Acquisition

  9. Hanne Tiesler – 9 Electric potential: Heat-equation: (Bioheat transfer eq.)‏ Source/Sink: Simulating RF Ablation

  10. Hanne Tiesler – 10 - Water content - Electric conductivity of native tissue - Heat capacity of dry tissue … Uncertainty in material properties • Material parameters are different for each patient • Material parameters in vivo are not known

  11. Hanne Tiesler – 11 Parameter Identification • Temperature distribution can be measured during the ablation • Temperature depends on the material parameters • Reconstruct the thermal conductivity and the electrical conductivity of the tissue from measurement data of the temperature distribution • Fit the temperature to the measured data

  12. Hanne Tiesler – 12 Objective functional • Inverse problem as an optimal control of semi-linear parabolic equation • Minimize • With the measured temperature and and regularization coefficients

  13. Hanne Tiesler – 13 Coupled constraints • Heat equation: • Potential equation:

  14. Hanne Tiesler – 14 Discretization • Finite element discretization in space leads to system of ODEs as constraints: • Minimize • subject to

  15. Hanne Tiesler – 15 Computation • Solve the optimization problem for and with a SQP-method • Heat equation and potential equation have an effect on the computation of the temperature only • Box-constraints for and • Solving with worhp, an SQP solver developed by AG Optimierung und optimale Steuerung at University Bremen

  16. Hanne Tiesler – 16 First approaches • one-dimensional model • simple heat equation, without perfusion and coefficients and • additional assumptions for and like constant or piecewise constant • artificial temperature data, knowledge of the optimal parameters

  17. Hanne Tiesler – 18 Iterations Results for constant parameters Error for lambda and sigma

  18. Hanne Tiesler – 19 Piecewise constant parameters lambda sigma Regularization coefficients = 0.3

  19. Hanne Tiesler – 21 Results for piecewise constant lambda Error for lambda vs number of optimization variables with and without regularization

  20. Hanne Tiesler – 23 Results for piecewise constant sigma Error for sigma vs number of optimization variables with and without regularization

  21. Hanne Tiesler – 24 Results for piecewise constant lambda with regularization terms

  22. Hanne Tiesler – 25 Results for piecewise constant sigma with regularization terms

  23. Hanne Tiesler – 26 Current work and Outlook • Implementation for 3-dimensional model and artificial tumor-data as well as real CT-data • temperature dependence of the material parameters and • Fitting to real temperature distribution

  24. Hanne Tiesler – 27 Thank you for your attention

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