Influence of stent-induced vessel deformation on hemodynamic feature of bloodstream inside ICA aneurysms

One of the effective treatment options for intracranial aneurysms is stent-assisted coiling. Though, previous works have demonstrated that stent usage would result in the deformation of the local vasculature. The effect of simple stent on the blood hemodynamics is still uncertain. In this work, hemodynamic features of the blood stream on four different ICA aneurysm with/without interventional are investigated. To estimate the relative impacts of vessel deformation, four distinctive ICA aneurysm is simulated by the one-way FSI technique. Four hemodynamic factors of aneurysm blood velocity, wall pressure and WSS are compared in the peak systolic stage to disclose the impact of defamation by the stent in two conditions. The stent usage would decrease almost all of the mentioned parameters, except for OSI. Stenting reduces neck inflow rate, while the effect of interventional was not consistent among the aneurysms. The deformation of an aneurysm has a strong influence on the hemodynamics of an aneurysm. This outcome is ignored by most of the preceding investigations, which focused on the pre-interventional state for studying the relationship between hemodynamics and stents. Present results show that the application of stent without coiling would improve most hemodynamic factors, especially when the deformation of the aneurysm is high enough

3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces

Optimization and validation of a new 3D-US imaging robot to detect, localize and quantify lower limb arterial stenoses

L’athérosclérose est une maladie qui cause, par l’accumulation de plaques lipidiques, le durcissement de la paroi des artères et le rétrécissement de la lumière. Ces lésions sont généralement localisées sur les segments artériels coronariens, carotidiens, aortiques, rénaux, digestifs et périphériques. En ce qui concerne l’atteinte périphérique, celle des membres inférieurs est particulièrement fréquente. En effet, la sévérité de ces lésions artérielles est souvent évaluée par le degré d’une sténose (réduction >50 % du diamètre de la lumière) en angiographie, imagerie par résonnance magnétique (IRM), tomodensitométrie ou échographie. Cependant, pour planifier une intervention chirurgicale, une représentation géométrique artérielle 3D est notamment préférable. Les méthodes d’imagerie par coupe (IRM et tomodensitométrie) sont très performantes pour générer une imagerie tridimensionnelle de bonne qualité mais leurs utilisations sont dispendieuses et invasives pour les patients. L’échographie 3D peut constituer une avenue très prometteuse en imagerie pour la localisation et la quantification des sténoses. Cette modalité d’imagerie offre des avantages distincts tels la commodité, des coûts peu élevés pour un diagnostic non invasif (sans irradiation ni agent de contraste néphrotoxique) et aussi l’option d’analyse en Doppler pour quantifier le flux sanguin. Étant donné que les robots médicaux ont déjà été utilisés avec succès en chirurgie et en orthopédie, notre équipe a conçu un nouveau système robotique d’échographie 3D pour détecter et quantifier les sténoses des membres inférieurs. Avec cette nouvelle technologie, un radiologue fait l’apprentissage manuel au robot d’un balayage échographique du vaisseau concerné. Par la suite, le robot répète à très haute précision la trajectoire apprise, contrôle simultanément le processus d’acquisition d’images échographiques à un pas d’échantillonnage constant et conserve de façon sécuritaire la force appliquée par la sonde sur la peau du patient. Par conséquent, la reconstruction d’une géométrie artérielle 3D des membres inférieurs à partir de ce système pourrait permettre une localisation et une quantification des sténoses à très grande fiabilité. L’objectif de ce projet de recherche consistait donc à valider et optimiser ce système robotisé d’imagerie échographique 3D. La fiabilité d’une géométrie reconstruite en 3D à partir d’un système référentiel robotique dépend beaucoup de la précision du positionnement et de la procédure de calibration. De ce fait, la précision pour le positionnement du bras robotique fut évaluée à travers son espace de travail avec un fantôme spécialement conçu pour simuler la configuration des artères des membres inférieurs (article 1 - chapitre 3). De plus, un fantôme de fils croisés en forme de Z a été conçu pour assurer une calibration précise du système robotique (article 2 - chapitre 4). Ces méthodes optimales ont été utilisées pour valider le système pour l’application clinique et trouver la transformation qui convertit les coordonnées de l’image échographique 2D dans le référentiel cartésien du bras robotisé. À partir de ces résultats, tout objet balayé par le système robotique peut être caractérisé pour une reconstruction 3D adéquate. Des fantômes vasculaires compatibles avec plusieurs modalités d’imagerie ont été utilisés pour simuler différentes représentations artérielles des membres inférieurs (article 2 - chapitre 4, article 3 - chapitre 5). La validation des géométries reconstruites a été effectuée à l`aide d`analyses comparatives. La précision pour localiser et quantifier les sténoses avec ce système robotisé d’imagerie échographique 3D a aussi été déterminée. Ces évaluations ont été réalisées in vivo pour percevoir le potentiel de l’utilisation d’un tel système en clinique (article 3- chapitre 5).Atherosclerosis is a disease caused by the accumulation of lipid deposits inducing the remodeling and hardening of the vessel wall, which leads to a progressive narrowing of arteries. These lesions are generally located on the coronary, carotid, aortic, renal, digestive and peripheral arteries. With regards to peripheral vessels, lower limb arteries are frequently affected. The severity of arterial lesions are evaluated by the stenosis degree (reduction > 50.0 % of the lumen diameter) using angiography, magnetic resonance angiography (MRA), computed tomography (CT) and ultrasound (US). However, to plan a surgical therapeutic intervention, a 3D arterial geometric representation is notably preferable. Imaging methods such as MRA and CT are very efficient to generate a three-dimensional imaging of good quality even though their use is expensive and invasive for patients. 3D-ultrasound can be perceived as a promising avenue in imaging for the location and the quantification of stenoses. This non invasive, non allergic (i.e, nephrotoxic contrast agent) and non-radioactive imaging modality offers distinct advantages in convenience, low cost and also multiple diagnostic options to quantify blood flow in Doppler. Since medical robots already have been used with success in surgery and orthopedics, our team has conceived a new medical 3D-US robotic imaging system to localize and quantify arterial stenoses in lower limb vessels. With this new technology, a clinician manually teaches the robotic arm the scanning path. Then, the robotic arm repeats with high precision the taught trajectory and controls simultaneously the ultrasound image acquisition process at even sampling and preserves safely the force applied by the US probe. Consequently, the reconstruction of a lower limb arterial geometry in 3D with this system could allow the location and quantification of stenoses with high accuracy. The objective of this research project consisted in validating and optimizing this 3D-ultrasound imaging robotic system. The reliability of a 3D reconstructed geometry obtained with 2D-US images captured with a robotic system depends considerably on the positioning accuracy and the calibration procedure. Thus, the positioning accuracy of the robotic arm was evaluated in the workspace with a lower limb-mimicking phantom design (article 1 - chapter 3). In addition, a Z-phantom was designed to assure a precise calibration of the robotic system. These optimal methods were used to validate the system for the clinical application and to find the transformation which converts image coordinates of a 2D-ultrasound image into the robotic arm referential. From these results, all objects scanned by the robotic system can be adequately reconstructed in 3D. Multimodal imaging vascular phantoms of lower limb arteries were used to evaluate the accuracy of the 3D representations (article 2 - chapter 4, article 3 - chapter 5). The validation of the reconstructed geometry with this system was performed by comparing surface points with the manufacturing vascular phantom file surface points. The accuracy to localize and quantify stenoses with the 3D-ultrasound robotic imaging system was also determined. These same evaluations were analyzed in vivo to perceive the feasibility of the study

Artificial Intelligence: Development and Applications in Neurosurgery

The last decade has witnessed a significant increase in the relevance of artificial intelligence (AI) in neuroscience. Gaining notoriety from its potential to revolutionize medical decision making, data analytics, and clinical workflows, AI is poised to be increasingly implemented into neurosurgical practice. However, certain considerations pose significant challenges to its immediate and widespread implementation. Hence, this chapter will explore current developments in AI as it pertains to the field of clinical neuroscience, with a primary focus on neurosurgery. Additionally included is a brief discussion of important economic and ethical considerations related to the feasibility and implementation of AI-based technologies in neurosciences, including future horizons such as the operational integrations of human and non-human capabilities

Apport de l'assistance par ordinateur lors de la pose d'endoprothèse aortique

The development of endovascular aortic procedures is growing. These mini-invasive techniques allow a reduction of surgical trauma, usually important in conventional open surgery. The technical limitations of endovascular repair are pushed to special aortic localizations which were in the past decade indication for open repair. Success and efficiency of such procedures are based on the development and the implementation of decision-making tools. This work aims to improve endovascular procedures thanks to a better utilization of pre and intraoperative imaging. This approach is in the line with the framework of computer-assisted surgery whose concepts are applied to vascular surgery. The optimization of endograft deployment is considered in three steps. The first part is dedicated to preoperative imaging analysis and shows the limits of the current sizing tools. The accuracy of a new measurement criterion is assessed (outer curvature length). The second part deals with intraoperative imaging and shows the contribution of augmented reality in endovascular aortic repair. In the last part, image guided surgery on soft tissues is addressed, especially the arterial deformations occurring during endovascular procedures which disprove rigid registration in fusion imaging. The use of finite element simulation to deal with this issue is presented. We report an original approach based on a predictive model of deformations using finite element simulation with geometrical and anatomo-mechanical patient specific parameters extracted from the preoperative CT-scan.Les techniques endovasculaires, particulièrement pour l’aorte, sont en plein essor en chirurgie vasculaire. Ces techniques mini-invasives permettent de diminuer l’agression chirurgicale habituellement importante lors de la chirurgie conventionnelle. Les limites techniques sont repoussées à certaines localisations de l’aorte qui étaient il y a encore peu de temps inaccessibles aux endoprothèses. Le succès et l’efficience de ces interventions reposent en partie sur l'élaboration et la mise en œuvre de nouveaux outils d'aide à la décision. Ce travail entend contribuer à l’amélioration des procédures interventionnelles aortiques grâce à une meilleure exploitation de l’imagerie pré et peropératoire. Cette démarche s’inscrit dans le cadre plus général des Gestes Médico-Chirurgicaux Assistés par Ordinateur, dont les concepts sont revisités pour les transposer au domaine de la chirurgie endovasculaire. Trois axes sont développés afin de sécuriser et optimiser la pose d'endoprothèse. Le premier est focalisé sur l’analyse préopératoire du scanner (sizing) et montre les limites des outils de mesure actuels et évalue la précision d’un nouveau critère de mesure des longueurs de l’aorte (courbure externe). Le deuxième axe se positionne sur le versant peropératoire et montre la contribution de la réalité augmentée dans la pose d’une endoprothèse aortique. Le troisième axe s’intéresse au problème plus général des interventions sur les tissus mous et particulièrement aux déformations artérielles qui surviennent au cours des procédures interventionnelles qui mettent en défaut le recalage rigide lors de la fusion d’images. Nous présentons une approche originale basée sur un modèle numérique de prédiction des déformations qui utilise la simulation par éléments finis en y intégrant des paramètres géométriques et anatomo-mécaniques spécifique-patient extraits du scanner préopératoire