305 research outputs found

    Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

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    Percutaneous thermal ablation has proved to be an effective modality for treating both benign and malignant tumors in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50 oC, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumor destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of- the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non- invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow

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

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    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

    Design-centric Method for an Augmented Reality Robotic Surgery

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    Master'sMASTER OF ENGINEERIN

    Modelling and Analysis of a new Integrated Radiofrequency Ablation and Division Device

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    Master'sMASTER OF ENGINEERIN

    COMPUTED-AIDED AND ROBOT-ASSISTED RADIOFREQUENCY ABLATION OF LARGE LIVER TUMOR

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    Ph.DDOCTOR OF PHILOSOPH

    Current state of the art of regional hyperthermia treatment planning: A review

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    Locoregional hyperthermia, i.e. increasing the tumor temperature to 40-45 °C using an external heating device, is a very effective radio and chemosensitizer, which significantly improves clinical outcome. There is a clear thermal dose-effect relation, but the pursued optimal thermal dose of 43 °C for 1 h can often not be realized due to treatment limiting hot spots in normal tissue. Modern heating devices have a large number of independent antennas, which provides flexible power steering to optimize tumor heating and minimize hot spots, but manual selection of optimal settings is difficult. Treatment planning is a very valuable tool to improve locoregional heating. This paper reviews the developments in treatment planning software for tissue segmentation, electromagnetic field calculations, thermal modeling and optimization techniques. Over the last decade, simulation tools have become more advanced. On-line use has become possible by implementing algorithms on the graphical processing unit, which allows real-time computations. The number of applications using treatment planning is increasing rapidly and moving on from retrospective analyses towards assisting prospective clinical treatment strategies. Some clinically relevant applications will be discussed

    Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases

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    Three-dimensional (3D) visualization involves feature extraction and 3D reconstruction of CT images using a computer processing technology. It is a tool for displaying, describing, and interpreting 3D anatomy and morphological features of organs, thus providing intuitive, stereoscopic, and accurate methods for clinical decision-making. It has played an increasingly significant role in the diagnosis and management of liver diseases. Over the last decade, it has been proven safe and effective to use 3D simulation software for pre-hepatectomy assessment, virtual hepatectomy, and measurement of liver volumes in blood flow areas of the portal vein; meanwhile, the use of 3D models in combination with hydrodynamic analysis has become a novel non-invasive method for diagnosis and detection of portal hypertension. We herein describe the progress of research on 3D visualization, its workflow, current situation, challenges, opportunities, and its capacity to improve clinical decision-making, emphasizing its utility for patients with liver diseases. Current advances in modern imaging technologies have promised a further increase in diagnostic efficacy of liver diseases. For example, complex internal anatomy of the liver and detailed morphological features of liver lesions can be reflected from CT-based 3D models. A meta-analysis reported that the application of 3D visualization technology in the diagnosis and management of primary hepatocellular carcinoma has significant or extremely significant differences over the control group in terms of intraoperative blood loss, postoperative complications, recovery of postoperative liver function, operation time, hospitalization time, and tumor recurrence on short-term follow-up. However, the acquisition of high-quality CT images and the use of these images for 3D visualization processing lack a unified standard, quality control system, and homogeneity, which might hinder the evaluation of application efficacy in different clinical centers, causing enormous inconvenience to clinical practice and scientific research. Therefore, rigorous operating guidelines and quality control systems need to be established for 3D visualization of liver to develop it to become a mature technology. Herein, we provide recommendations for the research on diagnosis and management of 3D visualization in liver diseases to meet this urgent need in this research field
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