Pulmonary diseases are frequently associated with changes in lung anatomy. These diseases may change the airway, vessel and lung tissue properties. In order to evaluate the lung in a longitudinal study, a stable reference system is required to identify corresponding parts of the lung. The structure of the airway tree can be used to repeatedly identify the regions of interest. In this study, an improved method for matching of intra-patient airway trees was proposed and evaluated. The association graph method proposed by Pelillo et al. matches free and rooted trees by detecting the maximal sub-tree isomorphism. Tschirren et al. implemented this approach for labeling and matching of human airway trees and reported 92.9% matching accuracy which is the highest among existing methods. However we recognized a few shortcomings of this method. When we tested it on seven normal human cases, we observed that successful matching relies heavily on the accurate labeling of main branchpoints in the trees. Incorrect labeling of main branch points or failure in labeling results in failure to match that branch point. Such matching errors may eventually propagate to sub-trees. On our seven data samples, matching accuracy was found to be as low as 65%. To improve the matching performance, we propose to make matching independent of labeling as well as improve association graph by adding constraint of path-length along with the existing constraints. Furthermore, we would like to redefine the incorrect matches as those matches which are mismatched as well as those that are missed by the matching algorithm. Our results for a total of 27 cases show a significant improvement in accuracy. The accuracy calculated as per the convention without accounting for the branchpoint pairs missed by the algorithm is 92.19% whereas the accuracy calculated as per our definition is 73.98%, with runtime in the range of 0.01-262.81 sec (average runtime is 25.14 sec). We thus propose an improved association graph method which is efficient in matching intra-patient airway trees with good accuracy and within a reasonable time.

Les travaux de cette thèse s’inscrivent dans le cadre du planning de traitements minimalement invasifs des lésions des artères coronaires. Le cardiologue réalise un examen coronarographique, puis dans la continuité, une angioplastie transluminale. L’angiographie rotationnelle à rayons X permet de visualiser sous différentes incidences 2D la lumière des artères coronaires sur plusieurs cycles cardiaques et aussi d’obtenir une reconstruction 3D+T des arbres coronaires. A partir de cette séquence, notre objectif est de déterminer automatiquement une incidence optimale 2D du segment sténosé compatible avec les angles du C-arm afin d’aider le cardiologue lors de l’intervention.Différentes étapes sont considérées pour calculer la position angulaire optimale du C-arm. Afin de suivre la zone de lésion durant le cycle cardiaque, une première méthode est proposée pour mettre en correspondance tous les arbres de la séquence 3D+T. Tout d’abord, un appariement deux à deux des arbres successifs est réalisé afin de construire un arbre d’union. Ces derniers sont ensuite fusionnés afin d’obtenir un arbre mosaïque représentant l’arbre le plus complet de la séquence. L’utilisation de mesures de similarités géométriques et hiérarchiques ainsi que l’insertion de nœuds artificiels permet de prendre en compte les différents mouvements non-rigides des artères coronaires subits au cours du cycle cardiaque et les variations topologiques dû à leurs extractions. Cet appariement nous permet de proposer une deuxième méthode afin d’obtenir une vue angiographique 2D optimale de la zone de lésion tout le long du cycle cardiaque. Cette incidence est proposée spécifiquement pour trois types de région d’intérêt (segment unique, segment multiple ou bifurcation) et est calculée à partir de quatre critères (raccourcissement, chevauchement interne et externe ou angle d’ouverture de bifurcation). Une vue 2D déployée du segment projeté avec le moins de superposition avec les structures vasculaires avoisinantes est obtenue. Nous donnons également la possibilité au cardiologue d’avoir une incidence optimale privilégiant soit le déploiement du stent ou soit le guidage d’outils de la racine de l’arbre à la zone sténosée. Nos différents algorithmes ont été évalués sur une séquence réelle de 10 phases segmentées à partir d’un CT et de 41 séquences simulées. / The thesis work deals with the planning of minimally invasive surgery of coronary artery lesions. The physician performs a coronarography following by a percutaneous transluminal angioplasty. The X-ray rotational angiography permits to visualize the lumen artery under different projection angles in several cardiac cycles. From these 2D projections, a 3D+T reconstruction of coronary arteries can be obtained. Our goal is to determine automatically from this 3D+T sequence, the optimal angiographic viewing angle of the stenotic segment. Several steps are proposed to compute the optimal angular position of the C-arm. Firstly, a mosaic-based tree matching algorithm of the 3D+T sequence is proposed to follow the stenotic lesion in the whole cardiac cycle. A pair-wise inexact tree matching is performed to build a tree union between successive trees. Next, these union trees are merged to obtain the mosaic tree which represents the most complete tree of the sequence. To take into account the non-rigid movement of coronary arteries during the cardiac cycle and their topology variations due to the 3D reconstruction or segmentation, similarity measures based on hierarchical and geometrical features are used. Artificial nodes are also inserted. With this global tree sequence matching, we propose secondly a new method to determine the optimal viewing angle of the stenotic lesion throughout the cardiac cycle. This 2D angiographic view which is proposed for three regions of interest (single segment, multiple segment or bifurcation) is computed from four criteria: the foreshortening, the external and internal overlap and the bifurcation opening angle rates. The optimal view shows the segment in its most extended and unobstructed dimension. This 2D view can be optimal either for the deployment of the stent or for the catheter guidance (from the root to the lesion). Our different algorithms are evaluated on real sequence (CT segmentation) and 41 simulated sequences.