Modélisation de l’ablation radiofréquence pour la planification de la résection de tumeurs abdominales

The outcome of radiofrequency ablation (RFA) of abdominal tumors is challenged by the presence of blood vessels and time-varying thermal conductivity, which make patient-specific planning extremely difficult. By providing predictive tools, biophysical models may help clinicians to plan and guide the procedure for an effective treatment. We introduce a detailed computational model of the biophysical mechanisms involved in RFA of hepatic tumors such as heat diffusion and cellular necrosis. It simulates the extent of ablated tissue based on medical images, from which patient-specific models of the liver, visible vessels and tumors are segmented. In this thesis, a new approach for solving these partial differential equations based on the Lattice Boltzmann Method is introduced. The model is first evaluated against clinical data of patients who underwent RFA of liver tumors. Then, a comprehensive pre-clinical experiment that combines multi-modal, pre- and post-operative anatomical and functional images, as well as the interventional monitoring of the temperature and delivered power is presented. This enables an end-to-end validation framework that considers the most comprehensive data set for model validation. Then, we automatically estimate patient-specific parameters to better predict the ablated tissue. This personalization strategy has been validated on 7 ablations from 3 clinical cases. From the pre-clinical study, we can go further in the personalization by comparing the simulated temperature and delivered power with the actual measurements during the procedure. These contributions have led to promising results, and open new perspectives in RFA guidance and planning.L'ablation par radiofréquence (ARF) de tumeurs abdominales est rendue difficile par l’influence des vaisseaux sanguins et les variations de la conductivité thermique, compliquant la planification spécifique à un patient donné. En fournissant des outils prédictifs, les modèles biophysiques pourraient aider les cliniciens à planifier et guider efficacement la procédure. Nous introduisons un modèle mathématique détaillé des mécanismes impliqués dans l’ARF des tumeurs du foie comme la diffusion de la chaleur et la nécrose cellulaire. Il simule l’étendue de l’ablation à partir d’images médicales, d’après lesquelles des modèles personnalisés du foie, des vaisseaux visibles et des tumeurs sont segmentés. Dans cette thèse, une nouvelle approche pour résoudre ces équations basée sur la méthode de Lattice Boltzmann est introduite. Le modèle est d’abord évalué sur des données de patients qui ont subi une ARF de tumeurs du foie. Ensuite, un protocole expérimental combinant des images multi-modales, anatomiques et fonctionnelles pré- et post-opératoires, ainsi que le suivi de la température et de la puissance délivrée pendant l'intervention est présenté. Il permet une validation totale du modèle qui considère des données les plus complètes possibles. Enfin, nous estimons automatiquement des paramètres personnalisés pour mieux prédire l'étendu de l’ablation. Cette stratégie a été validée sur 7 ablations dans 3 cas cliniques. A partir de l'étude préclinique, la personnalisation est améliorée en comparant les simulations avec les mesures faites durant la procédure. Ces contributions ont abouti à des résultats prometteurs, et ouvrent de nouvelles perspectives pour planifier et guider l’ARF

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

Computational modeling of radiofrequency ablation for the planning and guidance of abdominal tumor treatment

L'ablation par radiofréquence (ARF) de tumeurs abdominales est rendue difficile par l’influence des vaisseaux sanguins et les variations de la conductivité thermique, compliquant la planification spécifique à un patient donné. En fournissant des outils prédictifs, les modèles biophysiques pourraient aider les cliniciens à planifier et guider efficacement la procédure. Nous introduisons un modèle mathématique détaillé des mécanismes impliqués dans l’ARF des tumeurs du foie comme la diffusion de la chaleur et la nécrose cellulaire. Il simule l’étendue de l’ablation à partir d’images médicales, d’après lesquelles des modèles personnalisés du foie, des vaisseaux visibles et des tumeurs sont segmentés. Dans cette thèse, une nouvelle approche pour résoudre ces équations basée sur la méthode de Lattice Boltzmann est introduite. Le modèle est d’abord évalué sur des données de patients qui ont subi une ARF de tumeurs du foie. Ensuite, un protocole expérimental combinant des images multi-modales, anatomiques et fonctionnelles pré- et post-opératoires, ainsi que le suivi de la température et de la puissance délivrée pendant l'intervention est présenté. Il permet une validation totale du modèle qui considère des données les plus complètes possibles. Enfin, nous estimons automatiquement des paramètres personnalisés pour mieux prédire l'étendu de l’ablation. Cette stratégie a été validée sur 7 ablations dans 3 cas cliniques. A partir de l'étude préclinique, la personnalisation est améliorée en comparant les simulations avec les mesures faites durant la procédure. Ces contributions ont abouti à des résultats prometteurs, et ouvrent de nouvelles perspectives pour planifier et guider l’ARF.The outcome of radiofrequency ablation (RFA) of abdominal tumors is challenged by the presence of blood vessels and time-varying thermal conductivity, which make patient-specific planning extremely difficult. By providing predictive tools, biophysical models may help clinicians to plan and guide the procedure for an effective treatment. We introduce a detailed computational model of the biophysical mechanisms involved in RFA of hepatic tumors such as heat diffusion and cellular necrosis. It simulates the extent of ablated tissue based on medical images, from which patient-specific models of the liver, visible vessels and tumors are segmented. In this thesis, a new approach for solving these partial differential equations based on the Lattice Boltzmann Method is introduced. The model is first evaluated against clinical data of patients who underwent RFA of liver tumors. Then, a comprehensive pre-clinical experiment that combines multi-modal, pre- and post-operative anatomical and functional images, as well as the interventional monitoring of the temperature and delivered power is presented. This enables an end-to-end validation framework that considers the most comprehensive data set for model validation. Then, we automatically estimate patient-specific parameters to better predict the ablated tissue. This personalization strategy has been validated on 7 ablations from 3 clinical cases. From the pre-clinical study, we can go further in the personalization by comparing the simulated temperature and delivered power with the actual measurements during the procedure. These contributions have led to promising results, and open new perspectives in RFA guidance and planning

Système et méthode pour la simulation patient-spécifique interactive de thérapie d'ablation par radiofréquence

A method and system for interactive patient-specific simulation of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. A computational domain is generated from the patient-specific anatomical model of the liver. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the circulatory system of the liver by solving a bio-heat equation for each node on the level-set representation using a Lattice-Boltzmann method (LBM) implementation. Cellular necrosis in the liver is computed based on the simulated heat diffusion. Visualizations of a computed necrosis region and temperature maps of the liver are generated. A user input is interactively received to modify the position of the virtual ablation probe, the heat diffusion and cellular necrosis is re-simulated based on the user input, and the visualizations of the computed necrosis region and the temperature maps are updatedLa présente invention concerne un procédé et un système pour la simulation patient-spécifique interactive de thérapie d’ablation d’une tumeur hépatique. Le modèle anatomique patient-spécifique du système hépatique et circulatoire du foie est estimé à partir de données d’image médicale 3D d’un patient. Un domaine de calcul est généré à partir du modèle anatomique patient-spécifique du foie. La circulation sanguine dans le foie et le système circulatoire du foie est simulée sur la base du modèle anatomique patient-spécifique. La diffusion thermique due à l’ablation est simulée sur la base d’une position de sonde d’ablation virtuelle et la circulation sanguine simulée dans le foie et le système circulatoire du foie par résolution d’une équation biothermique pour chaque nœud sur la représentation définie par niveau au moyen d’une mise en œuvre du procédé de Lattice-Boltzmann (LBM). La nécrose cellulaire dans le foie est calculée sur la base de la diffusion thermique simulée. Des visualisations d’une région de nécrose calculée et des cartes de température du foie sont générées. Une entrée d’utilisateur est reçue de façon interactive pour modifier la position de la sonde d’ablation virtuelle, la diffusion thermique et la nécrose cellulaire sont re-simulées sur la base de l’entrée d’utilisateur, et les visualisations de la région de nécrose calculée et les cartes de température sont mises à jour

System and method for personalized computation of tissue ablation extent based on medical images

A method and system for personalized computation of tissue ablation extent based on medical images of a patient is disclosed. A patient-specific anatomical model of the liver and liver vessels is estimated from medical image data of a patient. Blood flow in the liver and liver vessels is simulated. An ablation simulation is performed that uses a bio-heat model to simulate heat diffusion due to an ablation based on the simulated blood flow and a cellular necrosis model to simulate cellular necrosis in the liver based on the simulated heat diffusion. Personalized tissue parameters of the bio-heat model and the cellular necrosis model are estimated based on observed results of a preliminary ablation procedure. Planning of the ablation procedure is then performed using the personalized bio-heat equation and the cellular necrosis model. The model can be subsequently refined as more ablation observations are obtained

System and method for patient specific modeling of liver tumor ablation

A method and system for tumor ablation planning and guidance based on a patient-specific model of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the venous system of the liver. Cellular necrosis in the liver is simulated based on the simulated heat diffusion. A visualization of a simulated necrosis region is generated and displayed to the user for decision making and optimal therapy planning and guidance

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

International audienceRadiofrequency 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