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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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

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.

Keywords

Graphical Processing Unit Hepatic Vein Radiofrequency Ablation Cartesian Grid Ablate Tissue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chloé Audigier
    • 1
    • 2
  • Tommaso Mansi
    • 2
  • Hervé Delingette
    • 1
  • Saikiran Rapaka
    • 2
  • Viorel Mihalef
    • 2
  • Puneet Sharma
    • 2
  • Daniel Carnegie
    • 4
  • Emad Boctor
    • 3
  • Michael Choti
    • 4
  • Ali Kamen
    • 2
  • Dorin Comaniciu
    • 2
  • Nicholas Ayache
    • 1
  1. 1.Asclepios Research GroupINRIA Sophia-AntipolisSophia-AntipolisFrance
  2. 2.Corporate Research and Technology, Imaging and Computer VisionSiemens CorporationPrincetonUSA
  3. 3.Dept. of RadiologyJohns Hopkins Medical InstitutionsBaltimoreUSA
  4. 4.Dept. of SurgeryJohns Hopkins Medical InstitutionsBaltimoreUSA

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