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

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

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

Master'sMASTER OF ENGINEERIN

Registration of ultrasound and computed tomography for guidance of laparoscopic liver surgery

Laparoscopic Ultrasound (LUS) imaging is a standard tool used for image-guidance during laparoscopic liver resection, as it provides real-time information on the internal structure of the liver. However, LUS probes are di cult to handle and their resulting images hard to interpret. Additionally, some anatomical targets such as tumours are not always visible, making the LUS guidance less e ective. To solve this problem, registration between the LUS images and a pre-operative Computed Tomography (CT) scan using information from blood vessels has been previously proposed. By merging these two modalities, the relative position between the LUS images and the anatomy of CT is obtained and both can be used to guide the surgeon. The problem of LUS to CT registration is specially challenging, as besides being a multi-modal registration, the eld of view of LUS is signi cantly smaller than that of CT. Therefore, this problem becomes poorly constrained and typically an accurate initialisation is needed. Also, the liver is highly deformed during laparoscopy, complicating the problem further. So far, the methods presented in the literature are not clinically feasible as they depend on manually set correspondences between both images. In this thesis, a solution for this registration problem that may be more transferable to the clinic is proposed. Firstly, traditional registration approaches comprised of manual initialisation and optimisation of a cost function are studied. Secondly, it is demonstrated that a globally optimal registration without a manual initialisation is possible. Finally, a new globally optimal solution that does not require commonly used tracking technologies is proposed and validated. The resulting approach provides clinical value as it does not require manual interaction in the operating room or tracking devices. Furthermore, the proposed method could potentially be applied to other image-guidance problems that require registration between ultrasound and a pre-operative scan

A Novel System and Image Processing for Improving 3D Ultrasound-guided Interventional Cancer Procedures

Image-guided medical interventions are diagnostic and therapeutic procedures that focus on minimizing surgical incisions for improving disease management and reducing patient burden relative to conventional techniques. Interventional approaches, such as biopsy, brachytherapy, and ablation procedures, have been used in the management of cancer for many anatomical regions, including the prostate and liver. Needles and needle-like tools are often used for achieving planned clinical outcomes, but the increased dependency on accurate targeting, guidance, and verification can limit the widespread adoption and clinical scope of these procedures. Image-guided interventions that incorporate 3D information intraoperatively have been shown to improve the accuracy and feasibility of these procedures, but clinical needs still exist for improving workflow and reducing physician variability with widely applicable cost-conscience approaches. The objective of this thesis was to incorporate 3D ultrasound (US) imaging and image processing methods during image-guided cancer interventions in the prostate and liver to provide accessible, fast, and accurate approaches for clinical improvements. An automatic 2D-3D transrectal ultrasound (TRUS) registration algorithm was optimized and implemented in a 3D TRUS-guided system to provide continuous prostate motion corrections with sub-millimeter and sub-degree error in 36 ± 4 ms. An automatic and generalizable 3D TRUS prostate segmentation method was developed on a diverse clinical dataset of patient images from biopsy and brachytherapy procedures, resulting in errors at gold standard accuracy with a computation time of 0.62 s. After validation of mechanical and image reconstruction accuracy, a novel 3D US system for focal liver tumor therapy was developed to guide therapy applicators with 4.27 ± 2.47 mm error. The verification of applicators post-insertion motivated the development of a 3D US applicator segmentation approach, which was demonstrated to provide clinically feasible assessments in 0.246 ± 0.007 s. Lastly, a general needle and applicator tool segmentation algorithm was developed to provide accurate intraoperative and real-time insertion feedback for multiple anatomical locations during a variety of clinical interventional procedures. Clinical translation of these developed approaches has the potential to extend the overall patient quality of life and outcomes by improving detection rates and reducing local cancer recurrence in patients with prostate and liver cancer

Projection-based Spatial Augmented Reality for Interactive Visual Guidance in Surgery

Ph.DDOCTOR OF PHILOSOPH