Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images

Chloé Audigier, Tommaso Mansi, Hervé Delingette, Saikiran Rapaka, Viorel Mihalef, Puneet Sharma, Daniel Carnegie, Emad Boctor, Michael Choti, Ali Kamen, Dorin Comaniciu, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages323-330
Number of pages8
Volume8151 LNCS
EditionPART 3
DOIs
StatePublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

Fingerprint

CT Image
Ablation
Lattice Boltzmann Method
Liver
Tumors
Tumor
Simulation
Vessel
Planning
Tissue
Thermal Diffusivity
Necrosis
Heat Diffusion
Blood Vessels
Thermal diffusivity
Blood vessels
Graphics Processing Unit
Blood Flow
Level Set
Recurrence

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Audigier, C., Mansi, T., Delingette, H., Rapaka, S., Mihalef, V., Sharma, P., ... Ayache, N. (2013). Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8151 LNCS, pp. 323-330). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_41

Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. / Audigier, Chloé; Mansi, Tommaso; Delingette, Hervé; Rapaka, Saikiran; Mihalef, Viorel; Sharma, Puneet; Carnegie, Daniel; Boctor, Emad; Choti, Michael; Kamen, Ali; Comaniciu, Dorin; Ayache, Nicholas.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. p. 323-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Audigier, C, Mansi, T, Delingette, H, Rapaka, S, Mihalef, V, Sharma, P, Carnegie, D, Boctor, E, Choti, M, Kamen, A, Comaniciu, D & Ayache, N 2013, Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8151 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 323-330, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-642-40760-4_41
Audigier C, Mansi T, Delingette H, Rapaka S, Mihalef V, Sharma P et al. Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8151 LNCS. 2013. p. 323-330. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-40760-4_41
Audigier, Chloé ; Mansi, Tommaso ; Delingette, Hervé ; Rapaka, Saikiran ; Mihalef, Viorel ; Sharma, Puneet ; Carnegie, Daniel ; Boctor, Emad ; Choti, Michael ; Kamen, Ali ; Comaniciu, Dorin ; Ayache, Nicholas. / Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. pp. 323-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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