Heart disease is the leading cause of death in the developed world.1 Cardiac pathologies include abnormal closure of the mitral valve,2 which can be treated by surgical operations, but the repair outcome varies greatly based on the experience of the surgeon. Simulating the procedure with a computer-based tool can greatly improve valve repair. Various teams are working on biomechanical models to compute the valve behaviour during peak systole.3–5 Although they use an accurate finite element method, they also use a tedious manual segmentation of the valve. Providing means to automatically segment the chordae and the leaflets would allow significant progress in the perspective of simulating the surgical gesture for the mitral valve repair. Valve chordae are generalized cylinders: Instead of being limited to a line, the central axis is a continuous curve. Instead of a constant radius, the radius varies along the axis. In most of the cases chordae sections are flattened ellipses and classical model-based methods commonly used for vessel enhancement6 or vessel segmentation7 fail. In this paper, we exploit the fact that there are no other generalized cylinders than the chordae in the micro CT scan and we propose a topology-based method for the chordae extraction. This approach is flexible and only requires the knowledge of an upper bound of the maximum chordae radius. Examples of segmentation are provided on three porcine datasets. The reliability of the segmentation is proved with a dataset where the ground truth is available.
In interventional radiology, navigating devices under the sole guidance of fluoroscopic images inside a complex architecture of tortuous and narrow vessels like the cerebral vascular tree is a difficult task. Visualizing the device in 3D could facilitate this navigation. For curvilinear devices such as guide-wires and catheters, a 3D reconstruction may be achieved using two simultaneous fluoroscopic views, as available on a biplane acquisition system. The purpose of this paper is to present a new automatic three-dimensional curve reconstruction method that has the potential to reconstruct complex 3D curves and does not require a perfect segmentation of the endovascular device. Using epipolar geometry, our algorithm translates the point correspondence problem into a segment correspondence problem. Candidate 3D curves can be formed and evaluated independently after identifying all possible combinations of compatible 3D segments. Correspondence is then inherently solved by looking in 3D space for the most coherent curve in terms of continuity and curvature. This problem can be cast into a graph problem where the most coherent curve corresponds to the shortest path of a weighted graph. We present quantitative results of curve reconstructions performed from numerically simulated projections of tortuous 3D curves extracted from cerebral vascular trees affected with brain arteriovenous malformations as well as fluoroscopic image pairs of a guide-wire from both phantom and clinical sets. Our method was able to select the correct 3D segments in 97.5% of simulated cases thus demonstrating its ability to handle complex 3D curves and can deal with imperfect 2D segmentation.
We present in this paper a preliminary study of rib motion tracking during Interventional Radiology (IR) fluoroscopy guided procedures. It consists in providing a physician with moving rib three-dimensional (3D) models projected in the fluoroscopy plane during a treatment. The strategy is to help to quickly recognize the target and the no-go areas i.e. the tumor and the organs to avoid.
The method consists in i) elaborating a kinematic model of each rib from a preoperative computerized tomography (CT) scan, ii) processing the on-line fluoroscopy image and iii) optimizing the parameters of the kinematic law such as the transformed 3D rib projected on the medical image plane fit well with the previously processed image.
The results show a visually good rib tracking that has been quantitatively validated by showing a periodic motion as well as a good synchronism between ribs.
The real time recovery of the projection geometry is a fundamental issue in interventional navigation applications (e.g. guide wire reconstruction, medical augmented reality). In most works, the intrinsic parameters are supposed to be constant and the extrinsic parameters (C-arm motion) are deduced either from the orientation sensors of the C-arm or from other additional sensors (eg. optical and/or electro-magnetic sensors). However, due to the weight of the X-ray tube and the C-arm, the system is undergoing deformations which induce variations of the intrinsic parameters as a function of the C-arm orientation. In our approach, we propose to measure the effects of the mechanical deformations onto the intrinsic parameters in a calibration procedure. Robust calibration methods exist (the gold standard is the multi-image calibration) but they are time consuming and too tedious to set up in a clinical context. For these reasons, we developed an original and easy to use method, based on a planar calibration target, which aims at measuring with a high level of accuracy the variation of the intrinsic parameters on a vascular C-arm. The precision of the planar-based method was evaluated by the mean of error propagation using techniques described in.8 It appeared that the precision of the intrinsic parameters are comparable to the one obtained from the multi-image calibration method. The planar-based method was also successfully used to assess to behavior of the C-arm with respect to the C-arm orientations. Results showed a clear variation of the principal point when the LAO/RAO orientation was changed. In contrast, the intrinsic parameters do not change during a cranio-caudal C-arm motion.
The radiotherapic treatment of cerebral arterious malformations (AVM) requires an accurate estimation of the AVM shape. This estimation is classically obtained from the delineation of the AVM in several 2D angiographic views. In this paper, a clinical study of the inter-observer variability in the AVM detection is first performed. It proves tha the estimated volume varie a lot between observers. For thee reasons, we propose a framewok for AVM delineation which makes use of 2D and 3D angiographic images: the initial estimate obtained with 2D angiographic images is then refined within the 3D volume using deformable models. Results are presented demonstrating shape delineation on various AVMs.
During an interventional neuroradiology exam, knowing the exact location of the catheter tip with respect to the patient can dramatically help the physician. An image registration between digital subtracted angiography (DSA) images and a volumic pre-operative image (magnetic resonance or computed tomography volumes) is a way to infer this important information. This mono-patient multimodality matching can be reduced to finding the projection matrix that transforms any voxel of the volume onto the DSA image plane. This modelization is unfortunately not valid in the case of distorted images, which is the case for DSA images. A classical angiography room can now generate 3D X-ray angiography volumes (3DXA). Since the DSA images are obtained with the same machine, it should be possible to deduce the projection matrix from the sensor data indicating the current machine position. We propose an interpolation scheme, associated to a pre-operative calibration of the machine that allows us to correct the distortions in the image at any position used during the exam with a precision of one pixel. Thereafter, we describe some calibration procedures and an associated model of the machine that can provide us with a projection matrix at any position of the machine. Thus, we obtain a machine-based 2D DSA/3DXA registration. The misregistration error can be limited to 2.5 mm if the patient is well centered within the system. This error is confirmed by a validation on a phantom of the vascular tree. This validation also yields that the residual error is a translation in the 3D space. As a consequence, the registration method presented in this paper can be used as an initial guess to an iterative refining algorithm.