233 research outputs found

    Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

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    Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

    Real-Time Quantum Noise Suppression In Very Low-Dose Fluoroscopy

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    Fluoroscopy provides real-time X-ray screening of patient's organs and of various radiopaque objects, which make it an invaluable tool for many interventional procedures. For this reason, the number of fluoroscopy screenings has experienced a consistent growth in the last decades. However, this trend has raised many concerns about the increase in X-ray exposure, as even low-dose procedures turned out to be not as safe as they were considered, thus demanding a rigorous monitoring of the X-ray dose delivered to the patients and to the exposed medical staff. In this context, the use of very low-dose protocols would be extremely beneficial. Nonetheless, this would result in very noisy images, which need to be suitably denoised in real-time to support interventional procedures. Simple smoothing filters tend to produce blurring effects that undermines the visibility of object boundaries, which is essential for the human eye to understand the imaged scene. Therefore, some denoising strategies embed noise statistics-based criteria to improve their denoising performances. This dissertation focuses on the Noise Variance Conditioned Average (NVCA) algorithm, which takes advantage of the a priori knowledge of quantum noise statistics to perform noise reduction while preserving the edges and has already outperformed many state-of-the-art methods in the denoising of images corrupted by quantum noise, while also being suitable for real-time hardware implementation. Different issues are addressed that currently limit the actual use of very low-dose protocols in clinical practice, e.g. the evaluation of actual performances of denoising algorithms in very low-dose conditions, the optimization of tuning parameters to obtain the best denoising performances, the design of an index to properly measure the quality of X-ray images, and the assessment of an a priori noise characterization approach to account for time-varying noise statistics due to changes of X-ray tube settings. An improved NVCA algorithm is also presented, along with its real-time hardware implementation on a Field Programmable Gate Array (FPGA). The novel algorithm provides more efficient noise reduction performances also for low-contrast moving objects, thus relaxing the trade-off between noise reduction and edge preservation, while providing a further reduction of hardware complexity, which allows for low usage of logic resources also on small FPGA platforms. The results presented in this dissertation provide the means for future studies aimed at embedding the NVCA algorithm in commercial fluoroscopic devices to accomplish real-time denoising of very low-dose X-ray images, which would foster their actual use in clinical practice

    Development of a Surgical Assistance System for Guiding Transcatheter Aortic Valve Implantation

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    Development of image-guided interventional systems is growing up rapidly in the recent years. These new systems become an essential part of the modern minimally invasive surgical procedures, especially for the cardiac surgery. Transcatheter aortic valve implantation (TAVI) is a recently developed surgical technique to treat severe aortic valve stenosis in elderly and high-risk patients. The placement of stented aortic valve prosthesis is crucial and typically performed under live 2D fluoroscopy guidance. To assist the placement of the prosthesis during the surgical procedure, a new fluoroscopy-based TAVI assistance system has been developed. The developed assistance system integrates a 3D geometrical aortic mesh model and anatomical valve landmarks with live 2D fluoroscopic images. The 3D aortic mesh model and landmarks are reconstructed from interventional angiographic and fluoroscopic C-arm CT system, and a target area of valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3D aortic mesh model, landmarks and target area of implantation onto fluoroscopic images is updated by approximating the aortic root motion from a pigtail catheter motion without contrast agent. A rigid intensity-based registration method is also used to track continuously the aortic root motion in the presence of contrast agent. Moreover, the aortic valve prosthesis is tracked in fluoroscopic images to guide the surgeon to perform the appropriate placement of prosthesis into the estimated target area of implantation. An interactive graphical user interface for the surgeon is developed to initialize the system algorithms, control the visualization view of the guidance results, and correct manually overlay errors if needed. Retrospective experiments were carried out on several patient datasets from the clinical routine of the TAVI in a hybrid operating room. The maximum displacement errors were small for both the dynamic overlay of aortic mesh models and tracking the prosthesis, and within the clinically accepted ranges. High success rates of the developed assistance system were obtained for all tested patient datasets. The results show that the developed surgical assistance system provides a helpful tool for the surgeon by automatically defining the desired placement position of the prosthesis during the surgical procedure of the TAVI.Die Entwicklung bildgeführter interventioneller Systeme wächst rasant in den letzten Jahren. Diese neuen Systeme werden zunehmend ein wesentlicher Bestandteil der technischen Ausstattung bei modernen minimal-invasiven chirurgischen Eingriffen. Diese Entwicklung gilt besonders für die Herzchirurgie. Transkatheter Aortenklappen-Implantation (TAKI) ist eine neue entwickelte Operationstechnik zur Behandlung der schweren Aortenklappen-Stenose bei alten und Hochrisiko-Patienten. Die Platzierung der Aortenklappenprothese ist entscheidend und wird in der Regel unter live-2D-fluoroskopischen Bildgebung durchgeführt. Zur Unterstützung der Platzierung der Prothese während des chirurgischen Eingriffs wurde in dieser Arbeit ein neues Fluoroskopie-basiertes TAKI Assistenzsystem entwickelt. Das entwickelte Assistenzsystem überlagert eine 3D-Geometrie des Aorten-Netzmodells und anatomischen Landmarken auf live-2D-fluoroskopische Bilder. Das 3D-Aorten-Netzmodell und die Landmarken werden auf Basis der interventionellen Angiographie und Fluoroskopie mittels eines C-Arm-CT-Systems rekonstruiert. Unter Verwendung dieser Aorten-Netzmodelle wird das Zielgebiet der Klappen-Implantation automatisch geschätzt. Mit Hilfe eines auf Template Matching basierenden Tracking-Ansatzes wird die Überlagerung des visualisierten 3D-Aorten-Netzmodells, der berechneten Landmarken und der Zielbereich der Implantation auf fluoroskopischen Bildern korrekt überlagert. Eine kompensation der Aortenwurzelbewegung erfolgt durch Bewegungsverfolgung eines Pigtail-Katheters in Bildsequenzen ohne Kontrastmittel. Eine starrere Intensitätsbasierte Registrierungsmethode wurde verwendet, um kontinuierlich die Aortenwurzelbewegung in Bildsequenzen mit Kontrastmittelgabe zu detektieren. Die Aortenklappenprothese wird in die fluoroskopischen Bilder eingeblendet und dient dem Chirurg als Leitfaden für die richtige Platzierung der realen Prothese. Eine interaktive Benutzerschnittstelle für den Chirurg wurde zur Initialisierung der Systemsalgorithmen, zur Steuerung der Visualisierung und für manuelle Korrektur eventueller Überlagerungsfehler entwickelt. Retrospektive Experimente wurden an mehreren Patienten-Datensätze aus der klinischen Routine der TAKI in einem Hybrid-OP durchgeführt. Hohe Erfolgsraten des entwickelten Assistenzsystems wurden für alle getesteten Patienten-Datensätze erzielt. Die Ergebnisse zeigen, dass das entwickelte chirurgische Assistenzsystem ein hilfreiches Werkzeug für den Chirurg bei der Platzierung Position der Prothese während des chirurgischen Eingriffs der TAKI bietet

    A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

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    Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods

    Recalage préservant la topologie des vaisseaux: application à la cardiologie interventionnelle

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    In percutaneous coronary interventions, integrating into the live fluoroscopic image vessel calcifications and occlusion information that are revealed in the pre-operative Computed Tomography Angiography can greatly improve guidance of the clinician. Fusing pre- and intra-operative information into a single space aims at taking advantage of two complementary modalities and requires a step of registration that must provide good alignment and relevant correspondences between them. Most of the existing 3D/2D vessel registration algorithms do not take into account the particular topology of the vasculature to be matched, resulting into pairings that may be topologically inconsistent along the vasculature.A first contribution consisted in a registration framework dedicated to curve matching, denoted the Iterative Closest Curve (ICC). Its main feature is to preserve the topological consistency along curves by taking advantage of the Frechet distance that not only computes the distance between two curves but also builds ordered pairings along them. A second contribution is a pairing procedure designed for the matching of a vascular tree structure that endorses its particular topology and that can easily take advantage of the ICC-framework. Centerlines of the 3D tree are matched to curves extracted from the 2D vascular graph while preserving the connectivity at 3D bifurcations. The matching criterion used to build the pairings takes into account the geometric distance and the resemblance between curves both based on a global formulation using the Frechet distance.To evaluate our approach we run experiments on a database composed of 63 clinical cases, measuring accuracy on real conditions and robustness with respect to a simulated displacement. Quantitative results have been obtained using two complementary measures that aim at assessing the results both geometrically and topologically, and quantify the resulting alignment error as well as the pairing error. The proposed method exhibits good results both in terms of pairing and alignment and demonstrates to be low sensitive to the rotations to be compensated (up to 30 degrees).Cette thèse s’inscrit dans le cadre de la cardiologie interventionnelle. Intégrer des informations telles que la position des calcifications ainsi que la taille et forme d’une occlusion dans les images fluoroscopiques constituerait un bénéfice pour le praticien. Ces informations, invisibles dans les images rayons-X pendant la procédure, sont présentes au sein du scanner CT préopératoire. La fusion de cette modalité avec la fluoroscopie apporterait une aide précieuse au guidage temps réel des outils interventionnels en bénéficiant des informations fournies par le CT. Cette fusion requiert une étape de recalage qui vise à aligner au mieux les deux modalités et fournir des correspondances pertinentes entre elles. La plupart des algorithmes de recalage 3D/2D de vaisseaux rencontrent des difficultés à construire des appariements anatomiquement pertinents, essentiellement à cause du manque de cohérence topologique le long du réseau vasculaire.Afin de résoudre ce problème, nous proposons dans cette thèse un cadre générique pour le recalage de structures curvilinéaires. L’algorithme qui en découle préserve la structure des courbes appariées. Les artères coronaires pouvant être représentées par un ensemble de courbes arrangées en arbre, nous proposons aussi une procédure d’appariement qui respecte cette structure. Le recalage d’un arbre 3D sur un graphe 2D est ainsi réalisé en assurant la préservation des connectivités aux bifurcations. Le choix de l’appariement est basé sur un critère prenant en compte la distance géométrique ainsi que la ressemblance entre courbes. Ce critère est évalué grâce à une forme modifiée de la distance de Fréchet.Une base de données de 63 cas cliniques a été utilisée à travers différentes expériences afin de prouver la robustesse et la précision de notre approche. Nous avons proposé deux mesures complémentaires visant à quantifier la qualité de l’alignement d’une part et des appariements engendrés d’autre part. La méthode proposée se montre précise pour les alignements de la projection du modèle CT et des artères coronaires observées dans les images angiographiques. De plus, les appariements obtenus sont anatomiquement pertinents et lálgorithme a prouvé sa robustesse face aux perturbations de la position initiale. Nous attribuons cette robustesse à la qualité des appariements construits au fur et à mesure des itérations

    Modeling, Analysis, and Control of a Mobile Robot for \u3ci\u3eIn Vivo\u3c/i\u3e Fluoroscopy of Human Joints during Natural Movements

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    In this dissertation, the modeling, analysis and control of a multi-degree of freedom (mdof) robotic fluoroscope was investigated. A prototype robotic fluoroscope exists, and consists of a 3 dof mobile platform with two 2 dof Cartesian manipulators mounted symmetrically on opposite sides of the platform. One Cartesian manipulator positions the x-ray generator and the other Cartesian manipulator positions the x-ray imaging device. The robotic fluoroscope is used to x-ray skeletal joints of interest of human subjects performing natural movement activities. In order to collect the data, the Cartesian manipulators must keep the x-ray generation and imaging devices accurately aligned while dynamically tracking the desired skeletal joint of interest. In addition to the joint tracking, this also requires the robotic platform to move along with the subject, allowing the manipulators to operate within their ranges of motion. A comprehensive dynamic model of the robotic fluoroscope prototype was created, incorporating the dynamic coupling of the system. Empirical data collected from an RGB-D camera were used to create a human kinematic model that can be used to simulate the joint of interest target dynamics. This model was incorporated into a computer simulation that was validated by comparing the simulation results with actual prototype experiments using the same human kinematic model inputs. The computer simulation was used in a comprehensive dynamic analysis of the prototype and in the development and evaluation of sensing, control, and signal processing approaches that optimize the subject and joint tracking performance characteristics. The modeling and simulation results were used to develop real-time control strategies, including decoupling techniques that reduce tracking error on the prototype. For a normal walking activity, the joint tracking error was less than 20 mm, and the subject tracking error was less than 140 mm

    3D Sequential Kinematics of the Femoro-Tibial Joint of Normal Knee from Multiple Bi-planar X-rays: Accuracy and Repeatability

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    Background: Several methods can be used to assess joint kinematics going from optoelectronic motion analysis to biplanar fluoroscopy. The aim of the present work was to evaluate the reliability of the use of biplane radiography to quantify the sequential 3D kinematics of the femoro-tibial joint. Methods: Bi-planar X-rays (EOS imaging) of 12 lower limbs (6 specimens in vitro and 6 subjects in vivo) were taken for various knee flexion angles. 3D personalized models of the femur and the tibia were registered on each pair ofviews. To quantify the bias, the kinematic parameters calculated from the registered models were compared to those obtained from the tripods embedded in the specimens. Intra and inter-operator repeatability of each parameter were assessed from the registrations made by 3 operators in vivo. Results: In vitro, the bias of the tibia pose estimation obtained from the registration method was inferior to 1.6 mm and 0.4â—¦. In vivo, the repeatability of the sequential kinematic parameters was inferior to 0.3â—¦, 2.1â—¦and 1.8â—¦, for respectively flexion, varus-valgus and medial-lateral rotation and inferior to 1.8 mm for translations. Conclusion: Compared to simple fluoroscopy, the accuracy of our method based on sequential images was of the same order of magnitude, with better results for the translation in the frontal plane. The low dose of radiation of the EOS system offers promising prospects for a clinical use of this method to assess the femoro-tibial sequential kinematics

    Feasibility of using Lodox to perform digital subtraction angiography

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    Bibliography: leaves 150-157.Many cases in trauma involve vessel imaging to determine integrity and the origin of lesions or blockages. Digital subtraction angiography (DSA) is a tool used to improve the clarity of the vessels being imaged for better and easier decision making in diagnostics and planning. Lodox, a low dose x-ray system developed by Debex (Pty) Ltd, a subsidiary of de Beers, was designed specifically for the trauma environment. It therefore follows that, if possible, a function so readily used in trauma, such as DSA, should be added to the imaging repertoire of an x-ray system designed for use in this environment. In this dissertation the feasibility of using Lodox to perform DSA is therefore explored. In doing so, the requirements of a trauma unit and the theory behind DSA were researched so as to obtain a better understanding into what would be required

    Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration

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    This thesis establishes a biomedical image analysis framework for the advanced visualization of biological structures. It consists of two important parts: 1) the segmentation of some structures of interest in 3D medical scans, and 2) the registration of patient-specific 3D models with 2D interventional images. Segmenting biological structures results in 3D computational models that are simple to visualize and that can be analyzed quantitatively. Registering a 3D model with interventional images permits to position the 3D model within the physical world. By combining the information from a 3D model and 2D interventional images, the proposed framework can improve the guidance of surgical intervention by reducing the ambiguities inherent to the interpretation of 2D images. Two specific segmentation problems are considered: 1) the segmentation of large structures with low frequency intensity nonuniformity, and 2) the detection of fine curvilinear structures. First, we directed our attention toward the segmentation of relatively large structures with low frequency intensity nonuniformity. Such structures are important in medical imaging since they are commonly encountered in MRI. Also, the nonuniform diffusion of the contrast agent in some other modalities, such as CTA, leads to structures of nonuniform appearance. A level-set method that uses a local-linear region model is defined, and applied to the challenging problem of segmenting brain tissues in MRI. The unique characteristics of the proposed method permit to account for important image nonuniformity implicitly. To the best of our knowledge, this is the first time a region-based level-set model has been used to perform the segmentation of real world MRI brain scans with convincing results. The second segmentation problem considered is the detection of fine curvilinear structures in 3D medical images. Detecting those structures is crucial since they can represent veins, arteries, bronchi or other important tissues. Unfortunately, most currently available curvilinear structure detection filters incur significant signal lost at bifurcations of two structures. This peculiarity limits the performance of all subsequent processes, whether it be understanding an angiography acquisition, computing an accurate tractography, or automatically classifying the image voxels. This thesis presents a new curvilinear structure detection filter that is robust to the presence of X- and Y-junctions. At the same time, it is conceptually simple and deterministic, and allows for an intuitive representation of the structure’s principal directions. Once a 3D computational model is available, it can be used to enhance surgical guidance. A 2D/3D non-rigid method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of the interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, which reduces the uncertainty inherent in 2D interventional images. A fully non-rigid registration model is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process. With a typical run time of less than 3 seconds, the algorithms are fast enough for interactive applications
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