Abstract
Mathematical modeling has the potential to assist radiofrequency ablation (RFA) of tumors as it enables prediction of the extent of ablation. However, the accuracy of the simulation is challenged by the material properties since they are patient-specific, temperature and space dependent. In this paper, we present a framework for patient-specific radiofrequency ablation modeling of multiple lesions in the case of metastatic diseases. The proposed forward model is based upon a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver which relies on patient images. We estimate the most sensitive material parameters, those need to be personalized from the available clinical imaging and data. The selected parameters are then estimated using inverse modeling such that the point-to-mesh distance between the computed necrotic area and observed lesions is minimized. Based on the personalized parameters, the ablation of the remaining lesions are predicted. The framework is applied to a dataset of seven lesions from three patients including pre- and post-operative CT images. In each case, the parameters were estimated on one tumor and RFA is simulated on the other tumor(s) using these personalized parameters, assuming the parameters to be spatially invariant within the same patient. Results showed significantly good correlation between predicted and actual ablation extent (average point-to-mesh errors of 4.03 mm).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
http://dakota.sandia.gov - multilevel framework for sensitivity analysis.
References
Hildebrand, P., Leibecke, T., Kleemann, M., Mirow, L., et al.: Influence of operator experience in radiofrequency ablation of malignant liver tumours on treatment outcome. Eur. J. Surg. Oncol. (EJSO) 32, 430–434 (2006)
Kim, Y.S., Rhim, H., Cho, O.K., Koh, B.H., Kim, Y.: Intrahepatic recurrence after percutaneous radiofrequency ablation of hepatocellular carcinoma: analysis of the pattern and risk factors. Eur. J. Radiol. 59, 432–441 (2006)
Altrogge, I., Preusser, T., Kroger, T., Haase, S., Patz, T., Kirby, R.M.: Sensitivity analysis for the optimization of radiofrequency ablation in the presence of material parameter uncertainty. Int. J. Uncertain. Quantification 2(3), 295–321 (2012)
Chen, X., Saidel, G.M.: Mathematical modeling of thermal ablation in tissue surrounding a large vessel. J. Biomech. 131, 011001 (2009)
Jiang, Y., Mulier, S., Chong, W., Diel Rambo, M., et al.: Formulation of 3D finite elements for hepatic radiofrequency ablation. IJMIC 9, 225–235 (2010)
Kröger, T., Pätz, T., Altrogge, I., Schenk, A., et al.: Fast estimation of the vascular cooling in RFA based on numerical simulation. Open Biomed. Eng. J. 4, 16–26 (2010)
Payne, S., Flanagan, R., Pollari, M., Alhonnoro, T., et al.: Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours. Philos. T. Roy. Soc. A 369, 4233–4254 (2011)
Audigier, C., Mansi, T., Delingette, H., Rapaka, S., Mihalef, V., Sharma, P., Carnegie, D., Boctor, E., Choti, M., Kamen, A., Comaniciu, D., Ayache, N.: Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 323–330. Springer, Heidelberg (2013)
Criminisi, A., Sharp, T., Blake, A.: GeoS: geodesic image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 99–112. Springer, Heidelberg (2008)
Guo, Z., Zhao, T.: Lattice-boltzmann model for incompressible flows through porous media. Phys. Rev. E 66, 036304 (2002)
Pan, C., Luo, L.S., Miller, C.T.: An evaluation of lattice boltzmann schemes for porous medium flow simulation. Comput. Fluids 35, 898–909 (2006)
Pennes, H.H.: Analysis of tissue and arterial blood temperatures in the resting human forearm. J. Appl. Physiol. 85, 5–34 (1998)
Klinger, H.: Heat transfer in perfused biological tissue I: general theory. B. Math. Biol. 36, 403–415 (1974)
O’Neill, D., Peng, T., Stiegler, P., Mayrhauser, U., et al.: A three-state mathematical model of hyperthermic cell death. Ann. Biomed. Eng. 39, 570–579 (2011)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast automatic heart chamber segmentation from 3d CT data using marginal space learning and steerable features. In: IEEE 11th International Conference on Computer Vision, 2007, ICCV 2007, pp. 1–8. IEEE (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Audigier, C. et al. (2014). Parameter Estimation for Personalization of Liver Tumor Radiofrequency Ablation. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham. https://doi.org/10.1007/978-3-319-13692-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-13692-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13691-2
Online ISBN: 978-3-319-13692-9
eBook Packages: Computer ScienceComputer Science (R0)