title: Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images.
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 60x faster than standard finite element method.
authors: INRIA Sophia-Antipolis, Asclepios Research Group, Sophia-Antipolis, France.
authors: Chloé
authors: C
authors: Audigier
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Tommaso
authors: T
authors: Mansi
authors: INRIA Sophia-Antipolis, Asclepios Research Group, Sophia-Antipolis, France.
authors: Hervé
authors: H
authors: Delingette
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Saikiran
authors: S
authors: Rapaka
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Viorel
authors: V
authors: Mihalef
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Puneet
authors: P
authors: Sharma
authors: Dept. of Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
authors: Daniel
authors: D
authors: Carnegie
authors: Dept. of Radiology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
authors: Emad
authors: E
authors: Boctor
authors: Dept. of Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
authors: Michael
authors: M
authors: Choti
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Ali
authors: A
authors: Kamen
authors: Siemens Corporation, Corporate Research and Technology, Imaging and Computer Vision, Princeton, NJ, USA.
authors: Dorin
authors: D
authors: Comaniciu
authors: INRIA Sophia-Antipolis, Asclepios Research Group, Sophia-Antipolis, France.
authors: Nicholas
authors: N
authors: Ayache
mesh_terms: D017115:Catheter Ablation / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000503:physiopathology* / Q000601:surgery*; D008954:Models, Biological; D018802:Patient-Centered Care / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome