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

No abstract

Abstract. Today, 3-D angiography volumes are routinely generated from rotational angiography sequences. In previous work [7], we have studied the precision reached by registering such volumes with classical 2-D angiography images, inferring this matching only from the sensors of the angiography machine. The error led by such a registration can be described as a 3-D rigid motion composed of a large translation and a small rotation. This paper describes the strategy we followed to correct this error. The angiography image is compared in a two-step process to the Maximum Intensity Projection (MIP) of the angiography volume. The first step provides most of the translation by maximizing the cross-correlation. The second step recovers the residual rigid-body motion, thanks to a modified optical flow technique. A fine analysis of the equations encountered in both steps allows for a speed-up of the calculations. This algorithm was validated on 17 images of a phantom, and 5 patients. The residual error was determined by manually indicating points of interest and was found to be around 1 mm.

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.

In Digital Subtraction Angiography, the use of an Image Intensifier as a detector introduces geometrical distortions in the images. For stereotactic applications, such as the irradiation of cerebral arteriovenous malformations, these distortions have necessarily to be corrected, and the accuracy of this correction has to be examined. As the distortions depend on many parameters that vary during an examination (such as magnetic field and spatial position of the acquisition chain), the correction accuracy must be defined as a function of the acquisition protocol. We have developed a correction method based on the calibration of geometrical distortions using an image of a grid phantom. An experimental study of the influence of acquisition parameters over the distortion has been performed. A protocol has been defined which ensures a correction accuracy of 0. 1 millimeter. Finally, we have studied the accuracy obtained in the 3D location of a target as a function of the accuracy of the distortion correction. The final precision allows the use of our method for digital X-Ray stereotactic applications.

Current surgical navigation systems offer sub-millimetric real-time localization, however they are expensive, require the use of invasive markers attached to the patient, and often add extra operation time. In this paper we propose an affordable markerless navigation approach, based on mid end depth sensors, as an alternative to answer medical applications needs in terms of accuracy and robustness. An algorithm called Fast Volumetric Reconstruction (FaVoR) implements a compute-efficient approach for real time 3D model registration based tracking, allowing computed 3D poses to be used for video scene augmentation. After early testing with a first proof-of-concept implementation, a preliminary accuracy evaluation was performed using a dynamic test bench, achieving an average 2mm registration error during tracking.

To evaluate the interest of 3D angiography in the diagnosis, treatment and follow-up of aneurysms with special regard with endovascular GDC treatment. Spin angiography with 44 subtracted views (512x512 image matrix) is first acquired with a rotation of a C-arm through 200 degrees at 40° per second in an angiographic room prototype LCV+ single plane (GEMS) . 3D reconstruction ART (Algebraic Reconstruction Technique) is automatically applied and interactive visualisation of the 3D angiography is available on a workstation (UltraSparc 2, Sun microsystems) 10 minutes after the spin angiography acquisition. 3D angiography is displayed in M JP, surface rendering and endovascular views (virtual angioscopy ). 52 2D and 3D angiographies were performed in 40 patients with 49 intracranial aneurysms whith the same protocol of acquisition (AP, lateral and spin angiography). Each 3D angiography (MIP and surface rendering) was analysed by 2 senior radiologists and compared with the corresponding 2D angiographies (AP, lateral and spin views). Artefacts due to GDC were present in 24 cases, but bothering in only 12 cases. Analysis of the aneurysm was better in MIP than in 2Din 32 cases, equivalent in 18 and worse in 2 due to patient movements during acquisition. Surface rendering gave additional information in 29 cases. The impact was judged important for decision making or choice of treatment in 25 cases. 3D angiography improves the analysis of the aneurysms, gives crucial information to make the decision for treatment and is now routinely used in our institution for the diagnosis of intracranial aneurysms and follow-up after GDC treatment.

In this work, we present a hybrid algorithm to automatically delineate the heart volume in 3D cardiac computed tomography (CT) datasets for the visualization of coronary arteries. Our work eliminates the tedious and time consuming step of manually removing obscuring structures around the heart (ribs, sternum, liver...). It quickly provides a clear and well defined view of the coronaries. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object. The proposed approach is based on the fitting of a geometric model of the heart to a set of automatically extracted 3D points lying on the heart shell. A novel two-stage fitting scheme is used to improve the robustness to the outliers. The fitting result is further refined using a Random Walker (RW) segmentation approach. Qualitative analysis of results obtained on a 70 exam database shows the efficiency and the accuracy of our approach.