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Audigier C, Mansi T, Delingette H, et al. Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors. IEEE Trans Med Imaging. 2015;34(7):1576-1589doi: 10.1109/TMI.2015.2406575.
Audigier, C., Mansi, T., Delingette, H., Rapaka, S., Mihalef, V., Carnegie, D., Boctor, E., Choti, M., Kamen, A., Ayache, N., & Comaniciu, D. (2015). Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors. IEEE transactions on medical imaging, 34(7), 1576-1589. https://doi.org/10.1109/TMI.2015.2406575
Audigier, Chloe, et al. "Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors." IEEE transactions on medical imaging vol. 34,7 (2015): 1576-1589. doi: https://doi.org/10.1109/TMI.2015.2406575
Audigier C, Mansi T, Delingette H, Rapaka S, Mihalef V, Carnegie D, Boctor E, Choti M, Kamen A, Ayache N, Comaniciu D. Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors. IEEE Trans Med Imaging. 2015 Jul;34(7):1576-1589. doi: 10.1109/TMI.2015.2406575. PMID: 30132760.
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IEEE Trans Med Imaging. 2015 Jul;34(7):1576-1589. doi: 10.1109/TMI.2015.2406575.

Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors.

IEEE transactions on medical imaging

Chloe Audigier, Tommaso Mansi, Herve Delingette, Saikiran Rapaka, Viorel Mihalef, Daniel Carnegie, Emad Boctor, Michael Choti, Ali Kamen, Nicholas Ayache, Dorin Comaniciu

PMID: 30132760 DOI: 10.1109/TMI.2015.2406575

Abstract

Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre- and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.

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