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

International audienceRadio-frequency ablation (RFA), the most widely used minimally invasive ablative therapy of liver cancer, is challenged by a lack of patient-specifi c planning. In particular, the presence of blood vessels and time varying thermal di ffusivity makes the prediction of the extent of the ablated tissue diffi cult. 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 diff usion, cellular necrosis and blood flow through vessels and liver is solved to estimate the extent of ablated tissue. After quantitative verifi cations 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 fi nite element method

Challenges to Validate Multi-physics Model of Liver Tumor Radiofrequency Ablation from Pre-clinical Data

International audienceThe planning and interventional guidance of liver tumor ra-diofrequency ablation (RFA) is difficult due to the cooling effect of large vessels and the large variability of tissue parameters. Subject-specific modeling of RFA is challenging as it requires the knowledge of model geometry and hemodynamics as well as the simulation of heat transfer and cell death mechanisms. In this paper, we propose to validate such a model from pre-operative multi-modal images and intra-operative signals (temperature and power) measured by the ablation device itself. In particular , the RFA computation becomes subject-specific after three levels of personalization: anatomical, heat transfer and a novel cellular necro-sis model. We propose an end-to-end pre-clinical validation framework that considers the most comprehensive dataset for model validation. This framework can also be used for parameter estimation and we evaluate its predictive power in order to fully assess the possibility to personalize our model in the future. Such a framework would therefore not require any necrosis information, thus better suited for clinical applications. We evaluated our approach on seven ablations from three healthy pigs. The predictive power of the model was tested: a mean point to mesh error between predicted and actual ablation extent of 3.5 mm was achieved

Comprehensive Pre-Clinical Evaluation of a Multi-physics Model of Liver Tumor Radiofrequency Ablation

International audiencePurpose: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). Methods: The RFA computation becomes subject-specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multi-modal, pre-and post-operative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. Results: To exploit this data set, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. Conclusion: This enables an end-to-end pre-clinical validation framework that considers the available data set

The marker level set method: applications to simulation of liquids

Interface advection methods are important tools with applications in computer graphics and computer vision, as well as in computational fluid dynamics and other engineering domains. The classic level set method in particular is one of the most widely used methods for interfacial advection; however, this method is less successful in tracking high-curvature regions and thin sheets, and it completely discards information tangential to the interface. In this thesis we introduce a new method for advection of interfaces by an external velocity field with improved performance in the problematic areas of the classic level set method mentioned above, and present several applications to simulation of liquids. We show that our method, which we term the Marker Level Set (MLS), provides an accurate, simple, and efficient alternative to the present technology for interfacial advection. Moreover, MLS features several capabilities that are quite important for computer graphics, such as automatic surface texture transport and an easy way of generating spray and small bubbles during simulation of liquids. We introduce as well a new MLS-based level set reinitialization procedure which gives improved performance over classical reinitialization procedures used in the context of the level set method alone.Ph.D.Includes bibliographical references (p. 63-66)