Detection of tubular structures in 3D images is an important issue for vascular medical imaging. We present in this paper a new approach for centerline detection and reconstruction of 3D tubular structures. Several models of vessels are introduced for estimating the sensitivity of the image second-order derivatives according to elliptical cross section, to curvature of the axis, or to partial volume effects. Our approach uses a multiscale analysis for extracting vessels of different sizes according to the scale. For a given model of vessel, we derive an analytic expression of the relationship between the radius of the structure and the scale at which it is detected. The algorithm gives both centerline extraction and radius estimation of the vessels allowing their reconstruction. The method has been tested on synthetic images, an image of a phantom, and real images, with encouraging results.

Abstract-Cardiovascular diseases remain the primary cause of death in developed countries. In most cases, exploration of possibly underlying coronary artery pathologies is performed using X-ray coronary angiography. Current clinical routine in coronary angiography is directly conducted in 2-D projection images from several static viewing angles. However, for diagnosis and treatment purposes, coronary artery reconstruction is highly suitable. The purpose of this study is to provide physicians with a 3-D model of coronary arteries, e.g. for absolute threedimensional measures for lesion assessment, instead of direct projective measures deduced from the images, which are highly dependent on the viewing angle. In this article, we propose a novel method to reconstruct coronary arteries from one single rotational X-ray projection sequence. As a side result, we also obtain an estimation of the coronary artery motion. Our method consists of 3 main consecutive steps: (1) 3-D reconstruction of coronary artery centerlines, including respiratory motion compensation, (2) coronary artery 4-D motion computation, and (3) 3-D tomographic reconstruction of coronary arteries, involving compensation for respiratory and cardiac motions. We present some experiments on clinical datasets, and the feasibility of a true 3-D Quantitative Coronary Analysis is demonstrated.

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Three-dimensional DSA is valuable for evaluating the potential for EVT, finding a working view, and performing accurate measurements.

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Abstract. In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vesselof-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case.

In this paper, we present a new method to perform 3D tomographic reconstruction of coronary arteries from cone-beam rotational x-ray angiography acquisitions. We take advantage of the precomputation of the coronary artery motion, modelled as a parametric 4D motion field. Contrary to data gating or data triggering approaches, we homogeneously use all available frames, independently of the cardiac phase. In addition, we artificially subtract angiograms from their background structures. Our method significantly improves the reconstruction, by removing both motion and background artefacts. We have successfully tested it on the datasets from a synthetic phantom and 10 patients.

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