38 research outputs found

    Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images

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    Despite its two-dimensional nature, X-ray angiography (XRA) has served as the gold standard imaging technique in the interventional cardiology for over five decades. Accordingly, demands for tools that could increase efficiency of the XRA procedure for the quantitative analysis of coronary arteries (CA) are constantly increasing. The aim of this study was to propose a novel procedure for three-dimensional modeling of CA from uncalibrated XRA projections. A comprehensive mathematical model of the image formation was developed and used with a robust genetic algorithm optimizer to determine the calibration parameters across XRA views. The frames correspondences between XRA acquisitions were found using a partial-matching approach. Using the same matching method, an efficient procedure for vessel centerline reconstruction was developed. Finally, the problem of meshing complex CA trees was simplified to independent reconstruction and meshing of connected branches using the proposed nonuniform rational B-spline (NURBS)-based method. Because it enables structured quadrilateral and hexahedral meshing, our method is suitable for the subsequent computational modelling of CA physiology (i.e. coronary blood flow, fractional flow reverse, virtual stenting and plaque progression). Extensive validations using digital, physical, and clinical datasets showed competitive performances and potential for further application on a wider scale

    Spatio-temporal data fusion in cerebral angiography

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 153-167).This thesis provides a framework for generating the previously unobtained high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences allow image feedback during medical procedures that can facilitate the detection and observation of stenosis, aneurysms, and clots. The 3D time series is constructed by fusing together a single static 3D image with one or more time sequence of 2D projections. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of non-zero support. Results are presented on both clinical and simulated phantom data sets. The 3D time series results are visualized using the following tools: time series of intensity slices, synthetic X-rays from an arbitrary view, time series of isosurfaces, and 3D surfaces that show arrival times of contrast using color. This thesis also details the different steps needed to prepare the two classes of data. In addition to the spatio-temporal data fusion algorithm, three new algorithms are presented: a single pass groupwise registration algorithm for registering the time series, a 2D-3D registration algorithm for registering the time series with respect to the 3D volume, and a modified adaptive version of the Cusum algorithm used for determining arrival times of contrast within the 2D time sequences.by Andrew David Copeland.Ph.D

    3D reconstruction of coronary arteries from angiographic sequences for interventional assistance

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    Introduction -- Review of literature -- Research hypothesis and objectives -- Methodology -- Results and discussion -- Conclusion and future perspectives

    Reconstruction of coronary arteries from X-ray angiography: A review.

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    Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research

    3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography

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    Three-dimensional (3D) information on blood flow and vessel morphology is important when assessing cerebrovascular disease and when monitoring interventions. Rotational angiography is nowadays routinely used to determine the geometry of the cerebral vasculature. To this end, contrast agent is injected into one of the supplying arteries and the x-ray system rotates around the head of the patient while it acquires a sequence of x-ray images. Besides information on the 3D geometry, this sequence also contains information on blood flow, as it is possible to observe how the contrast agent is transported by the blood. The main goal of this thesis is to exploit this information for the quantitative analysis of blood flow. I propose a model-based method, called flow map fitting, which determines the blood flow waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a model of contrast agent transport to determine the flow parameters from the spatio-temporal progression of the contrast agent concentration, represented by a flow map. Furthermore, it overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some projection angles) of the c-arm using a reliability map. For the flow quantification, small changes to the clinical protocol of rotational angiography are desirable. These, however, hamper the standard 3D reconstruction. Therefore, a new method for the 3D reconstruction of the vessel morphology which is tailored to this application is also presented. To the best of my knowledge, I have presented the first quantitative results for blood flow quantification from rotational angiography. Additionally, the model-based approach overcomes several problems which are known from flow quantification methods for planar angiography. The method was mainly validated on images from different phantom experiments. In most cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between 10% and 15% for the blood flow waveform. Additionally, the applicability of the flow model was shown on clinical images from planar angiographic acquisitions. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions

    Coronary Artery Segmentation and Motion Modelling

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    Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

    Segmentation and skeletonization techniques for cardiovascular image analysis

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    Reconstruction 3D+T des artères coronaires à partir d'une séquence rotationnelle angiographique

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    Les maladies coronariennes sont une cause fréquente de décès dans les pays industrialisés. Au lieu d’avoir recours aux chirurgies traditionnelles à coeur ouvert, les cardiologues ont recours aux interventions percutanées, qui consistent en l'insertion du cathéter par l'artère fémorale. Le cathéter est dirigé vers les artères affectées, et l'angiographie coronaire est employée pour naviguer dans les structures vasculaires complexes. Par contre, l'angiographie 2D ne permet pas de discerner la profondeur, et son utilisation est fréquente durant une intervention, nécessitant ainsi une importante quantité d'agent de contraste et une exposition prolongée aux rayons X. Pour mitiger l'impact de ces problèmes, des techniques d'imagerie médicale sont employées pour assister le cardiologue lors de l'intervention, notamment la modélisation 3D+t des artères coronaires reconstruites à partir d'une séquence rotationnelle angiographique. Ces volumes permettent au chirurgien de guider le cathéter en 3D de manière claire, ainsi que limiter l'utilisation de rayons X et d'agent de contraste en effectuant l'intervention plus rapidement. La méthode proposée dans cette étude contient d'abord une segmentation multi-échelles par composants connectés pour extraire les artères coronaires et éliminer le bruit occasionné par les artéfacts d'arrière-plan comme les côtes et les poumons. Pour éliminer le décalage occasionné par le mouvement respiratoire, le déplacement est ensuite modélisé grâce à un modèle mathématique cyclique qui synchronise les images angiographiques de la séquence rotationnelle. La reconstruction 3D est ensuite effectuée en appariant les bifurcations des images pour en reconstruire les segments d'artères. Les pixels résultants sont appariés grâce à la géométrie épipolaire et une minimisation d'énergie par parcours de graphe. Une déformation affine par optimisation non linéaire du modèle reconstruit s'en suit pour obtenir une fluidité temporelle. La méthode est ainsi entièrement automatique, et les résultats démontrent qu'en présence de mouvement cardiaque et respiratoire, la reconstruction est robuste et est évaluable en contexte interventionnel

    Model-Based Visualization for Intervention Planning

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    Computer support for intervention planning is often a two-stage process: In a first stage, the relevant segmentation target structures are identified and delineated. In a second stage, image analysis results are employed for the actual planning process. In the first stage, model-based segmentation techniques are often used to reduce the interaction effort and increase the reproducibility. There is a similar argument to employ model-based techniques for the visualization as well. With increasingly more visualization options, users have many parameters to adjust in order to generate expressive visualizations. Surface models may be smoothed with a variety of techniques and parameters. Surface visualization and illustrative rendering techniques are controlled by a large set of additional parameters. Although interactive 3d visualizations should be flexible and support individual planning tasks, appropriate selection of visualization techniques and presets for their parameters is needed. In this chapter, we discuss this kind of visualization support. We refer to model-based visualization to denote the selection and parameterization of visualization techniques based on \u27a priori knowledge concerning visual perception, shapes of anatomical objects and intervention planning tasks

    Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

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    A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails
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