Suivi des vaisseaux sanguins en temps réel à partir d’images ultrasonores mode-B et reconstruction 3D : application à la caractérisation des sténoses artérielles

La maladie des artères périphériques (MAP) se manifeste par une réduction (sténose) de la lumière de l’artère des membres inférieurs. Elle est causée par l’athérosclérose, une accumulation de cellules spumeuses, de graisse, de calcium et de débris cellulaires dans la paroi artérielle, généralement dans les bifurcations et les ramifications. Par ailleurs, la MAP peut être causée par d`autres facteurs associés comme l’inflammation, une malformation anatomique et dans de rares cas, au niveau des artères iliaques et fémorales, par la dysplasie fibromusculaire. L’imagerie ultrasonore est le premier moyen de diagnostic de la MAP. La littérature clinique rapporte qu’au niveau de l’artère fémorale, l’écho-Doppler montre une sensibilité de 80 à 98 % et une spécificité de 89 à 99 % à détecter une sténose supérieure à 50 %. Cependant, l’écho-Doppler ne permet pas une cartographie de l’ensemble des artères des membres inférieurs. D’autre part, la reconstruction 3D à partir des images échographiques 2D des artères atteintes de la MAP est fortement opérateur dépendant à cause de la grande variabilité des mesures pendant l’examen par les cliniciens. Pour planifier une intervention chirurgicale, les cliniciens utilisent la tomodensitométrie (CTA), l’angiographie par résonance magnétique (MRA) et l’angiographie par soustraction numérique (DSA). Il est vrai que ces modalités sont très performantes. La CTA montre une grande précision dans la détection et l’évaluation des sténoses supérieures à 50 % avec une sensibilité de 92 à 97 % et une spécificité entre 93 et 97 %. Par contre, elle est ionisante (rayon x) et invasive à cause du produit de contraste, qui peut causer des néphropathies. La MRA avec injection de contraste (CE MRA) est maintenant la plus utilisée. Elle offre une sensibilité de 92 à 99.5 % et une spécificité entre 64 et 99 %. Cependant, elle sous-estime les sténoses et peut aussi causer une néphropathie dans de rares cas. De plus les patients avec stents, implants métalliques ou bien claustrophobes sont exclus de ce type d`examen. La DSA est très performante mais s`avère invasive et ionisante. Aujourd’hui, l’imagerie ultrasonore (3D US) s’est généralisée surtout en obstétrique et échocardiographie. En angiographie il est possible de calculer le volume de la plaque grâce à l’imagerie ultrasonore 3D, ce qui permet un suivi de l’évolution de la plaque athéromateuse au niveau des vaisseaux. L’imagerie intravasculaire ultrasonore (IVUS) est une technique qui mesure ce volume. Cependant, elle est invasive, dispendieuse et risquée. Des études in vivo ont montré qu’avec l’imagerie 3D-US on est capable de quantifier la plaque au niveau de la carotide et de caractériser la géométrie 3D de l'anastomose dans les artères périphériques. Par contre, ces systèmes ne fonctionnent que sur de courtes distances. Par conséquent, ils ne sont pas adaptés pour l’examen de l’artère fémorale, à cause de sa longueur et de sa forme tortueuse. L’intérêt pour la robotique médicale date des années 70. Depuis, plusieurs robots médicaux ont été proposés pour la chirurgie, la thérapie et le diagnostic. Dans le cas du diagnostic artériel, seuls deux prototypes sont proposés, mais non commercialisés. Hippocrate est le premier robot de type maitre/esclave conçu pour des examens des petits segments d’artères (carotide). Il est composé d’un bras à 6 degrés de liberté (ddl) suspendu au-dessus du patient sur un socle rigide. À partir de ce prototype, un contrôleur automatisant les déplacements du robot par rétroaction des images échographiques a été conçu et testé sur des fantômes. Le deuxième est le robot de la Colombie Britannique conçu pour les examens à distance de la carotide. Le mouvement de la sonde est asservi par rétroaction des images US. Les travaux publiés avec les deux robots se limitent à la carotide. Afin d’examiner un long segment d’artère, un système robotique US a été conçu dans notre laboratoire. Le système possède deux modes de fonctionnement, le mode teach/replay (voir annexe 3) et le mode commande libre par l’utilisateur. Dans ce dernier mode, l’utilisateur peut implémenter des programmes personnalisés comme ceux utilisés dans ce projet afin de contrôler les mouvements du robot. Le but de ce projet est de démontrer les performances de ce système robotique dans des conditions proches au contexte clinique avec le mode commande libre par l’utilisateur. Deux objectifs étaient visés: (1) évaluer in vitro le suivi automatique et la reconstruction 3D en temps réel d’une artère en utilisant trois fantômes ayant des géométries réalistes. (2) évaluer in vivo la capacité de ce système d'imagerie robotique pour la cartographie 3D en temps réel d'une artère fémorale normale. Pour le premier objectif, la reconstruction 3D US a été comparée avec les fichiers CAD (computer-aided-design) des fantômes. De plus, pour le troisième fantôme, la reconstruction 3D US a été comparée avec sa reconstruction CTA, considéré comme examen de référence pour évaluer la MAP. Cinq chapitres composent ce mémoire. Dans le premier chapitre, la MAP sera expliquée, puis dans les deuxième et troisième chapitres, l’imagerie 3D ultrasonore et la robotique médicale seront développées. Le quatrième chapitre sera consacré à la présentation d’un article intitulé " A robotic ultrasound scanner for automatic vessel tracking and three-dimensional reconstruction of B-mode images" qui résume les résultats obtenus dans ce projet de maîtrise. Une discussion générale conclura ce mémoire. L’article intitulé " A 3D ultrasound imaging robotic system to detect and quantify lower limb arterial stenoses: in vivo feasibility " de Marie-Ange Janvier et al dans l’annexe 3, permettra également au lecteur de mieux comprendre notre système robotisé. Ma contribution dans cet article était l’acquisition des images mode B, la reconstruction 3D et l’analyse des résultats pour le patient sain.

3D Face Reconstruction from 2D Pictures: First Results of a Web-Based Computer Aided System for Aesthetic Procedures

The human face is a vital component of our identity and many people undergo medical aesthetics procedures in order to achieve an ideal or desired look. However, communication between physician and patient is fundamental to understand the patient’s wishes and to achieve the desired results. To date, most plastic surgeons rely on either “free hand” 2D drawings on picture printouts or computerized picture morphing. Alternatively, hardware dependent solutions allow facial shapes to be created and planned in 3D, but they are usually expensive or complex to handle. To offer a simple and hardware independent solution, we propose a web-based application that uses 3 standard 2D pictures to create a 3D representation of the patient’s face on which facial aesthetic procedures such as filling, skin clearing or rejuvenation, and rhinoplasty are planned in 3D. The proposed application couples a set of well-established methods together in a novel manner to optimize 3D reconstructions for clinical use. Face reconstructions performed with the application were evaluated by two plastic surgeons and also compared to ground truth data. Results showed the application can provide accurate 3D face representations to be used in clinics (within an average of 2 mm error) in less than 5 min.

Lost in translation (and rotation) : rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Handling sectional views in volume-based approach to automatically construct 3D solid from 2D views

This paper presents an algorithm for solid model reconstruction from 2D sectional views based on volume-based approach. None of the existing work in automatic reconstruction from 2D orthographic views have addressed sectional views in detail. It is believed that the volume-based approach is better suited to handle different types of sectional views. The volume-based approach constructs the 3D solid by a boolean combination of elementary solids. The elementary solids are formed by sweep operation on loops identified in the input views. The only adjustment to be made for the presence of sectional views is in the identification of loops that would form the elemental solids. In the algorithm, the conventions of engineering drawing for sectional views, are used to identify the loops correctly. The algorithm is simple and intuitive in nature. Results have been obtained for full sections, offset sections and half sections. Future work will address other types of sectional views such as removed and revolved sections and broken-out sections. (C) 2004 Elsevier Ltd. All rights reserved.

A MatLAB Based Virtual Phantom for 2D Electrical Impedance Tomography (MatVP2DEIT): Studying the Medical Electrical Impedance Tomography Reconstruction in Computer

Electrical Impedance Tomography (EIT) is a computerized medical imaging technique which reconstructs the electrical impedance images of a domain under test from the boundary voltage-current data measured by an EIT electronic instrumentation using an image reconstruction algorithm. Being a computed tomography technique, EIT injects a constant current to the patient's body through the surface electrodes surrounding the domain to be imaged (Omega) and tries to calculate the spatial distribution of electrical conductivity or resistivity of the closed conducting domain using the potentials developed at the domain boundary (partial derivative Omega). Practical phantoms are essentially required to study, test and calibrate a medical EIT system for certifying the system before applying it on patients for diagnostic imaging. Therefore, the EIT phantoms are essentially required to generate boundary data for studying and assessing the instrumentation and inverse solvers a in EIT. For proper assessment of an inverse solver of a 2D EIT system, a perfect 2D practical phantom is required. As the practical phantoms are the assemblies of the objects with 3D geometries, the developing of a practical 2D-phantom is a great challenge and therefore, the boundary data generated from the practical phantoms with 3D geometry are found inappropriate for assessing a 2D inverse solver. Furthermore, the boundary data errors contributed by the instrumentation are also difficult to separate from the errors developed by the 3D phantoms. Hence, the errorless boundary data are found essential to assess the inverse solver in 2D EIT. In this direction, a MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate boundary data for assessing the 2D-EIT inverse solvers and the image reconstruction accuracy. MatVP2DEIT is a MatLAB-based computer program which simulates a phantom in computer and generates the boundary potential data as the outputs by using the combinations of different phantom parameters as the inputs to the program. Phantom diameter, inhomogeneity geometry (shape, size and position), number of inhomogeneities, applied current magnitude, background resistivity, inhomogeneity resistivity all are set as the phantom variables which are provided as the input parameters to the MatVP2DEIT for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. Boundary data sets are generated with different phantom configurations obtained with the different combinations of the phantom variables and the resistivity images are reconstructed using EIDORS. Boundary data of the virtual phantoms, containing inhomogeneities with complex geometries, are also generated for different current injection patterns using MatVP2DEIT and the resistivity imaging is studied. The effect of regularization method on the image reconstruction is also studied with the data generated by MatVP2DEIT. Resistivity images are evaluated by studying the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed phantom domain. Results show that the MatVP2DEIT generates accurate boundary data for different types of single or multiple objects which are efficient and accurate enough to reconstruct the resistivity images in EIDORS. The spatial resolution studies show that, the resistivity imaging conducted with the boundary data generated by MatVP2DEIT with 2048 elements, can reconstruct two circular inhomogeneities placed with a minimum distance (boundary to boundary) of 2 mm. It is also observed that, in MatVP2DEIT with 2048 elements, the boundary data generated for a phantom with a circular inhomogeneity of a diameter less than 7% of that of the phantom domain can produce resistivity images in EIDORS with a 1968 element mesh. Results also show that the MatVP2DEIT accurately generates the boundary data for neighbouring, opposite reference and trigonometric current patterns which are very suitable for resistivity reconstruction studies. MatVP2DEIT generated data are also found suitable for studying the effect of the different regularization methods on reconstruction process. Comparing the reconstructed image with an original geometry made in MatVP2DEIT, it would be easier to study the resistivity imaging procedures as well as the inverse solver performance. Using the proposed MatVP2DEIT software with modified domains, the cross sectional anatomy of a number of body parts can be simulated in PC and the impedance image reconstruction of human anatomy can be studied.

Ultrasonic imaging of 3D displacement vectors using a simulated 2D array and beamsteering.

Most quasi-static ultrasound elastography methods image only the axial strain, derived from displacements measured in the direction of ultrasound propagation. In other directions, the beam lacks high resolution phase information and displacement estimation is therefore less precise. However, these estimates can be improved by steering the ultrasound beam through multiple angles and combining displacements measured along the different beam directions. Previously, beamsteering has only considered the 2D case to improve the lateral displacement estimates. In this paper, we extend this to 3D using a simulated 2D array to steer both laterally and elevationally in order to estimate the full 3D displacement vector over a volume. The method is tested on simulated and phantom data using a simulated 6-10MHz array, and the precision of displacement estimation is measured with and without beamsteering. In simulations, we found a statistically significant improvement in the precision of lateral and elevational displacement estimates: lateral precision 35.69μm unsteered, 3.70μm steered; elevational precision 38.67μm unsteered, 3.64μm steered. Similar results were found in the phantom data: lateral precision 26.51μm unsteered, 5.78μm steered; elevational precision 28.92μm unsteered, 11.87μm steered. We conclude that volumetric 3D beamsteering improves the precision of lateral and elevational displacement estimates.

Ultrasonic imaging of 3D displacement vectors using a simulated 2D array and beamsteering

Most quasi-static ultrasound elastography methods image only the axial strain, derived from displacements measured in the direction of ultrasound propagation. In other directions, the beam lacks high resolution phase information and displacement estimation is therefore less precise. However, these estimates can be improved by steering the ultrasound beam through multiple angles and combining displacements measured along the different beam directions. Previously, beamsteering has only considered the 2D case to improve the lateral displacement estimates. In this paper, we extend this to 3D using a simulated 2D array to steer both laterally and elevationally in order to estimate the full 3D displacement vector over a volume. The method is tested on simulated and phantom data using a simulated 6-10 MHz array, and the precision of displacement estimation is measured with and without beamsteering. In simulations, we found a statistically significant improvement in the precision of lateral and elevational displacement estimates: lateral precision 35.69 μm unsteered, 3.70 μm steered; elevational precision 38.67 μm unsteered, 3.64 μm steered. Similar results were found in the phantom data: lateral precision 26.51 μm unsteered, 5.78 μm steered; elevational precision 28.92 μm unsteered, 11.87 μm steered. We conclude that volumetric 3D beamsteering improves the precision of lateral and elevational displacement estimates. © 2012 Elsevier B.V. All rights reserved.

Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Model

An improved technique for 3D head tracking under varying illumination conditions is proposed. The head is modeled as a texture mapped cylinder. Tracking is formulated as an image registration problem in the cylinder's texture map image. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. To solve the registration problem in the presence of lighting variation and head motion, the residual error of registration is modeled as a linear combination of texture warping templates and orthogonal illumination templates. Fast and stable on-line tracking is achieved via regularized, weighted least squares minimization of the registration error. The regularization term tends to limit potential ambiguities that arise in the warping and illumination templates. It enables stable tracking over extended sequences. Tracking does not require a precise initial fit of the model; the system is initialized automatically using a simple 2D face detector. The only assumption is that the target is facing the camera in the first frame of the sequence. The formulation is tailored to take advantage of texture mapping hardware available in many workstations, PC's, and game consoles. The non-optimized implementation runs at about 15 frames per second on a SGI O2 graphic workstation. Extensive experiments evaluating the effectiveness of the formulation are reported. The sensitivity of the technique to illumination, regularization parameters, errors in the initial positioning and internal camera parameters are analyzed. Examples and applications of tracking are reported.

Determining the parametric effectiveness of a CAD model

The motivation for this paper is to present an approach for rating the quality of the parameters in a computer-aided design model for use as optimization variables. Parametric Effectiveness is computed as the ratio of change in performance achieved by perturbing the parameters in the optimum way, to the change in performance that would be achieved by allowing the boundary of the model to move without the constraint on shape change enforced by the CAD parameterization. The approach is applied in this paper to optimization based on adjoint shape sensitivity analyses. The derivation of parametric effectiveness is presented for optimization both with and without the constraint of constant volume. In both cases, the movement of the boundary is normalized with respect to a small root mean squared movement of the boundary. The approach can be used to select an initial search direction in parameter space, or to select sets of model parameters which have the greatest ability to improve model performance. The approach is applied to a number of example 2D and 3D FEA and CFD problems.

2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.