Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers

International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of $0.81 ± 0.53 mm$, for the 3D mean distance at the segment of $0.37 ± 0.17$ mm and an average 3D tip error of $0.24 ± 0.13 mm$. For the real data-set,we obtained an average 3D Hausdorff distance of $1.74 ± 0.77 mm$, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of $0.53 ± 0.09 mm$. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions

Robust RANSAC-based blood vessel segmentation

International audienceMany vascular clinical applications require a vessel segmentation process that is able to both extract the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithms from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detect the vessel centerline; 2) provide a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Testing (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 30 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction

Constrained Stochastic State Estimation for 3D Shape Reconstruction of Catheters and Guidewires in Fluoroscopic Images

Minimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions between the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterization, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained an average 3D Hausdorff distance of 0.07 ± 0.37 mm; the 3D distance at the tip equal to 0.021 ± 0.009 mm and the 3D mean distance at the distal segment of the catheter equal to 0.02 ± 0.008 mm. For the real data-set, the obtained average 3D Hausdorff Distance was of 0.95 ± 0.35 mm, the average 3D distance at the tip is equal to 0.7 ± 0.45 mm with an average 3D mean distance at the distal segment of 0.7 ± 0.46 mm. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: errors on the friction coefficient, ambiguous views and non-linear complex phenomena such as stick and slip motions

Refining the 3D surface of blood vessels from a reduced set of 2D DSA images

International audienceNumerical simulations, such as blood flow or coil deployment in an intra-cranial aneurism, are very sensitive to the boundary conditions given by the surface of the vessel walls. Despite the undisputable high quality of 3D vascular imaging modalities, artifacts and noise still hamper the extraction of this surface with enough accuracy. Previous studies took the a priori that a homogeneous object was considered to make the reconstruction from the Xray images more robust. Here, an active surface approach is described, that does not depend on any particular image similarity criterion and grounds on high speed computation of the criterion derivatives. Mean square error and normalized cross-correlation are used to successfully demonstrate our algorithm on real images acquired on an anthropomorphic phantom. Preliminary results of coil deployment simulation are also given

Médiation en sciences du numériques : un levier pour comprendre notre quotidien ?

National audiencePour ne pas seulement consommer les produits numériques mais pouvoir les maîtriser et les co-créer, chacun doit développer une culture liée au numérique. Ainsi, s'initier au codage, apprendre et manipuler concrètement des notions comme celle d'information ou d'algorithme, partager les fondements du numérique... sont des actions essentielles. la médiation peut servir de catalyseur, à condition de bien en concevoir l'objet, identifier ses objectifs ainsi que ses moyens. Quoi ? Pourquoi ? Comment ? Ces trois questions fondamentales donnent un cadre initial à la réflexion sur les enjeux scientifiques et sociétaux de la médiation en sciences du numérique aujourd'hui. Quoi ? L'informatique, ou plutôt les Sciences du Numérique, ne sont pas qu'une technologie mais aussi une science à part entière. Pourquoi ? Les Sciences du Numérique ont des connexions extrêmement vastes, allant des objets de la vie quotidienne à de nombreux domaines scientifiques. Mais l'informatique est jeune est mal connue. Comment ? Chercheurs et médiateurs professionnels doivent collaborer et proposer des actions et supports en rupture avec la perception dominante qui est focalisée sur l'usage et la complexité

Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers

International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of $0.81 ± 0.53 mm$, for the 3D mean distance at the segment of $0.37 ± 0.17$ mm and an average 3D tip error of $0.24 ± 0.13 mm$. For the real data-set,we obtained an average 3D Hausdorff distance of $1.74 ± 0.77 mm$, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of $0.53 ± 0.09 mm$. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions

Réalité augmentée pour la chirurgie minimalement invasive du foie utilisant un modèle biomécanique guidé par l'image

National audienceCet article présente une méthode de réalité augmentée pour la chirurgie minimalement invasive du foie. Le réseau vasculaire et les tumeurs internes reconstruites à partir des données pré-opératoires (IRM ou CT) peuvent ainsi être visualisées dans l'image laparoscopique afin de guider les gestes du chirurgien pendant l'opération. Cette méthode est capable de propager les déformations 3D de la surface du foie à ses structures internes grâce à un modèle biomécanique sous-jacent qui prend en compte l'anisotropie et l'hétérogénéité du tissu hépatique. Des résultats sont montrés sur une vidéo in-vivo d'un foie humain acquise pendant une opération et sur un foie en silicone

Reconstruction robuste des vaisseaux sanguins par surfaces implicites locales

National audienceGrowing interest in computer-based simulations has arisen, specially for interventional radiology procedures. In this context, we address the problem of blood vessel segmentation from 3D rotational angiography data, as an implicit surface reconstruction problem. A new implicit model is proposed, made of a tree of local implicit surfaces. A dedicated tracking algorithm is described to build the tree. Meanwhile, robust extraction of points on the vessel surface is performed. An original surface approximation method is then described to locally fit these points. Tracking results are presented on a patient data. Also experimental analysis of our model is carried out on synthetic 2D examples. Finally, preliminary segmentation results are reported on a silicon vascular phantom.Les simulateurs informatiques suscitent un intérêt croissant, notamment dans le domaine de la radiologie interventionnelle. Dans ce contexte, nous abordons le problème de segmentation des vaisseaux sanguins par reconstruction de surfaces implicites à partir d'acquisitions d'angiographie rotationnelle 3D. Nous proposons un nouveau modèle implicite sous forme d'un arbre de fonctions implicites locales. L'arbre est bâti grâce à un algorithme dédié de suivi. Ce faisant des points sont extraits de manière robuste sur la surface vasculaire. Chaque fonction implicite est ensuite estimée avec une méthode originale pour approximer ces points. Enfin, nous présentons des résultats de suivi sur un patient, ainsi qu'une analyse expérimentale de notre modèle sur des exemples synthétiques en 2D pour finir par des résultats préliminaires de segmentation sur des données réelles de fantôme vasculaire

Time to Go Augmented in Vascular Interventional Neuroradiology?

International audienceThis editorial paper reports on our experience in introducing augmented reality (AR) in interventional neuroradiology environments. Our expectations about the next AR tools, in particular for more advanced visualization, are also put forward. For practical reasons, the references will be restricted to our contributions. For further information, the last recommendations concerning the medical management of aneurysm induced hemorrhages can be found in [connolly12

Multimodal fusion of electromagnetic, ultrasound and MRI data for building an articulatory model

International audienceData fusion from multiple sensors is of significant interest to the speech research community, as it can potentially provide a better picture of speech production through the use of complementary sensor modalities. This paper deals with the practical aspects of this problem, such as acquisition and processing of the dynamic US and EM data of the tongue during speech production, static MRI images of the vocal tract using repetitions, and registration of the data from these different sources to a common reference frame. To the best of our knowledge, this is the first work that demonstrates the potential of static and dynamic data fusion in the construction of articulatory databases