MICCAI 2013 - LNCS 8149-8151

Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Chloé Audigier^{1, 2}, Tommaso Mansi², Hervé Delingette¹, Saikiran Rapaka², Viorel Mihalef², Puneet Sharma², Daniel Carnegie⁴, Emad Boctor³, Michael Choti⁴, Ali Kamen², Dorin Comaniciu², and Nicholas Ayache¹

¹INRIA Sophia-Antipolis, Asclepios Research Group, Sophia-Antipolis, France

²Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA

³Dept. of Radiology, Johns Hopkins Medical Institutions, Baltimore, MD, USA

⁴Dept. of Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA

Abstract. Radio-frequency ablation (RFA), the most widely used minimally invasive ablative therapy of liver cancer, is challenged by a lack of patient-specific planning. In particular, the presence of blood vessels and time-varying thermal diffusivity makes the prediction of the extent of the ablated tissue difficult. This may result in incomplete treatments and increased risk of recurrence. We propose a new model of the physical mechanisms involved in RFA of abdominal tumors based on Lattice Boltzmann Method to predict the extent of ablation given the probe location and the biological parameters. Our method relies on patient images, from which level set representations of liver geometry, tumor shape and vessels are extracted. Then a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver is solved to estimate the extent of ablated tissue. After quantitative verifications against an analytical solution, we apply our framework to 5 patients datasets which include pre- and post-operative CT images, yielding promising correlation between predicted and actual ablation extent (mean point to mesh errors of 8.7 mm). Implemented on graphics processing units, our method may enable RFA planning in clinical settings as it leads to near real-time computation: 1 minute of ablation is simulated in 1.14 minutes, which is almost 60 × faster than standard finite element method.

LNCS 8151, p. 323 ff.

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