Yves Trousset

Model-Based Detection of Tubular Structures in 3D Images

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.

Geometrical calibration of x-ray imaging chains for three-dimensional reconstruction

Reconstructing a three-dimensional (3D) object from a set of its two-dimensional (2D) X-ray projections requires that the source position and image plane orientation in 3D space be obtained with high accuracy. We present a method for estimating the geometrical parameters of an X-ray imaging chain, based on the minimization of the reprojection mean quadratic error measured on reference points of a calibration phantom. This error is explicitly calculated with respect to the geometrical parameters of the conic projection, and a conjugate gradient technique is used for its minimization. By comparison to the classical unconstrained method, better results were obtained in simulation with our method, specially when only a few reference points are available. This method may be adapted to different X-ray systems and may also be extended to the estimation of the geometrical parameters of the imaging chain trajectory in the case of dynamic acquisitions.

Geometrical calibration for 3D x-ray imaging

Reconstructing a 3D structure from a set of its 2D X-ray projections requires that the source position and image plane orientation in 3D space be obtained with high accuracy for each of the imaging chain positions. This knowledge is generally obtained through a geometrical calibration of the data acquisition system. In this paper, we present a fully automatic method for such a geometrical calibration, well suited to a 3D X-ray imaging system which acquires sets of 2D projections during a rotation of the imaging chain. This method is based on (1) the use of a dedicated calibration phantom with reference points, (2) an automatic algorithm to detect and identify the reference points on the phantoms 2D X-Ray projections, and (3) the estimation of the imaging chain geometrical parameters by minimizing the reprojection mean quadratic error measured on these reference points. Results obtained both from simulation data and from data acquired on an experimental bench are presented.

Matching 3D MR angiography data and 2D X-ray angiograms

In this paper we present a new algorithm for matching a 3D MR angiography volume image with two 2D X-ray angiograms without using artificial markers. The goal is to prove the feasibility of such a technique, and the long-term aim is to be able to report the catheter position in a 3D pre-operative image, during an X-ray angiographic examination. First, vessels centerlines are computed using a novel algorithm. Then matching is performed using an extension of the ICP algorithm. Special care is taken in dealing with outliers. Comparing the results on real data of our algorithm with a stereotactic frame-based registration, the accuracy of our method is proved to be better than 2 mm in the worse case with good repeatability. This makes it usable for clinical applications.

Model of a vascular C-arm for 3D augmented fluoroscopy in interventional radiology

This paper deals with the modeling of a vascular C-arm to generate 3D augmented fluoroscopic images in an interventional radiology context. A methodology based on the use of a multi-image calibration is proposed to assess the physical behavior of the C-arm. From the knowledge of the main characteristics of the C-arm, realistic models of the acquisition geometry are proposed. Their accuracy was evaluated and experiments showed that the C-arm geometry can be predicted with a mean 2D reprojection error of 0.5 mm. The interest of 3D augmented fluoroscopy is also assessed on a clinical case.

Three-dimensional coronary arteriography.

In this paper we present a new imaging technique for three-dimensional (3-D) X-ray coronary arteriography. The goal is to provide in near to real-time a 3-D representation of the coronary arterial tree, helpful to better understand its topology and locate the possible lesions.The 3-D reconstruction of the coronary arteries is obtained from a set of X-ray conic projections acquired during a rotation of the imaging chain around the patient. Images are taken before and after injection of contrast agent. A subset of mask and opacified images is selected, corresponding to the same phase in the cardiac cycle. These images are subtracted and corrected for geometric distortion. The reconstruction is performed by using a two-step non-parametric detection/estimation method.Due to heart motion and propagation of the contrast agent, the number of available projections is very small. Typically 4 or 6 projections are available if the opacification is stable during 2 or 3 cardiac cycles and when using a biplane acquisition system.High resolution 5123 reconstructions of the coronary arteries from a cadaver heart are presented, with a voxel size of 0.4 mm. The 3-D reconstruction provides a good 3-D representation of the global structure, even with a number of projections as small as 4.

Multiscale cone-beam x-ray reconstruction

We address the problem of reconstructing a three-dimensional volume from a set of two-dimensional X-ray projections. We present a time efficient solution based on a multiscale estimation technique. Estimation is first performed at a coarse resolution. Then the resolution is increased step by step and at each step a new estimation is performed, using an initial value derived from the volume estimated at the preceding level of resolution. The method is illustrated by results obtained on geometric and anatomic phantoms.

Rectification for cone-beam projection and backprojection

The purpose of this paper is to derive a technique for accelerating the computation of cone-beam forward and backward projections that are the basic steps of tomographic reconstruction. The cone-beam geometry of C-arm systems is commonly described with projection matrices. Such matrices provide a continuous framework for analyzing the flow of operations needed to compute backprojection for analytical reconstruction, as well as the combination of forward and backward projections for iterative reconstruction. The proposed rectification technique resamples the original data to planes that are aligned with two of the reconstructed volume main axes, so that the original cone-beam geometry can be replaced by a simpler geometry, where succession of plane magnifications are involved only. Rectification generalizes previous independent results to the cone-beam backprojection of preprocessed data as well as to cone-beam iterative reconstruction. The memory access pattern of simple magnifications provides superior predictability and is, therefore, easier to optimize, independently of the choice of the interpolation technique. Rectification is also shown to provide control over interpolation errors through oversampling, allowing tradeoffs between computation speed and precision to be set. Experimental results are provided for linear and nearest neighbor interpolations, based on simulations, as well as phantom and patient data acquired on a digital C-arm system

3D Reconstruction of High Contrast Objects Using a Multi-scale Detection / Estimation Scheme

Reconstructing a three-dimensional (3D) volume from a set of twodimensional X-ray projections raises theoretical, instrumental and computational difficulties. Focused on high contrast objects, solutions are proposed for the successive steps of a 3D reconstruction procedure, from the raw measurements on an image intensifier up to the reconstruction algorithm based on a multi-scale detection / estimation scheme.

Three-dimensional x-ray angiography: first in-vivo results with a new system

A new system has been designed and built to validate the concept of 3D Computerized Angiography (3D CA). It addresses all the difficulties met from the acquisition of raw data on a patient to the display of the reconstructed volume. The main characteristics of the system and its operating modes are described. Special attention is paid to data processing aspects. The first in vivo results obtained with this system are presented.