Suscripción a Biblioteca: Guest
Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones
International Journal for Uncertainty Quantification
Factor de Impacto: 4.911 Factor de Impacto de 5 años: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Imprimir: 2152-5080
ISSN En Línea: 2152-5099

Acceso abierto

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2012004135
pages 295-321

SENSITIVITY ANALYSIS FOR THE OPTIMIZATION OF RADIOFREQUENCY ABLATION IN THE PRESENCE OF MATERIAL PARAMETER UNCERTAINTY

Inga Altrogge
Center of Complex Systems and Visualization, University of Bremen, Germany
Tobias Preusser
Fraunhofer MEVIS; and School of Engineering and Science, Jacobs University, Bremen, Germany
Tim Kroger
Georg Simon Ohm University of Applied Sciences, Nuremberg, Germany
Sabrina Haase
Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany
Torben Patz
School of Engineering and Science, Jacobs University Bremen; Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany
Mike Kirby
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA

SINOPSIS

We present a sensitivity analysis of the optimization of the probe placement in radiofrequency (RF) ablation which takes the uncertainty associated with biophysical tissue properties (electrical and thermal conductivity) into account. Our forward simulation of RF ablation is based upon a system of partial differential equations (PDEs) that describe the electric potential of the probe and the steady state of the induced heat. The probe placement is optimized by minimizing a temperature-based objective function such that the volume of destroyed tumor tissue is maximized. The resulting optimality system is solved with a multilevel gradient descent approach. By evaluating the corresponding optimality system for certain realizations of tissue parameters (i.e., at certain, well-chosen points in the stochastic space) the sensitivity of the system can be analyzed with respect to variations in the tissue parameters. For the interpolation in the stochastic space we use an adaptive sparse grid collocation (ASGC) approach presented by Ma and Zabaras. We underscore the significance of the approach by applying the optimization to CT data obtained from a real RF ablation case.


Articles with similar content:

MODEL REDUCTION FOR LARGE-SCALE EARTHQUAKE SIMULATION IN AN UNCERTAIN 3D MEDIUM
International Journal for Uncertainty Quantification, Vol.10, 2020, issue 2
O. Le Maître, Pierre Sochala, F. De Martin
SOLVING TRANSIENT NONLINEAR HEAT CONDUCTION PROBLEMS BY PROPER ORTHOGONAL DECOMPOSITION AND FEM
ICHMT DIGITAL LIBRARY ONLINE, Vol.11, 2004, issue
Adam Fic, Alain J. Kassab, Ryszard A. Bialecki
HYPERDIFFERENTIAL SENSITIVITY ANALYSIS OF UNCERTAIN PARAMETERS IN PDE-CONSTRAINED OPTIMIZATION
International Journal for Uncertainty Quantification, Vol.10, 2020, issue 3
Roland Herzog, Joseph Hart, Bart van Bloemen Waanders
GOAL-ORIENTED MODEL ADAPTIVITY IN STOCHASTIC ELASTODYNAMICS: SIMULTANEOUS CONTROL OF DISCRETIZATION, SURROGATE MODEL AND SAMPLING ERRORS
International Journal for Uncertainty Quantification, Vol.10, 2020, issue 3
Pedro Bonilla-Villalba, A. Kundu, S. Claus, Pierre Kerfriden
ANALYSIS OF VARIANCE-BASED MIXED MULTISCALE FINITE ELEMENT METHOD AND APPLICATIONS IN STOCHASTIC TWO-PHASE FLOWS
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 6
Guang Lin, Yalchin Efendiev, Lijian Jiang, Jia Wei