Anne Rougee

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.

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.

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.

A new system for 3D computerized X-ray angiography: First in vivo results

A new system has been designed and built to validate the concept of 3D computerized Χ-ray angiography. It addresses all the difficulties met from the acquisition of raw data on a patient to the display of the reconstructed volume. The first in vivo results obtained with this system are presented.

Three-Dimensional Computerized Angiography

A new technique for 3-D angiography is presented. Its purpose is to provide fully automatic, high-resolution 3-D imaging of blood vessels. The 3-D vascular structure is reconstructed from a set of 2-D X-ray conic projections acquired around the patient, using a time-efficient multi-resolution detection/estimation scheme.

3-D Reconstruction Of High Contrast Objects From Limited Conic X-Ray Projections

We address the problem of reconstructing a 3-D object using a few of its conic projections measured with a 2-D X-ray detector. This problem is ill-posed and prior information must therefore be used to regularize the solution. We propose a non-parametric method based on a detection-estimation scheme that is particulary well-suited to the reconstruction of sparse objects such as 3-D vascular structures. We present results of 3-D reconstruction obtained from both computer simulated and experimentally measured projections.

Quantitative 2-D dual energy imaging: towards 3-D selective reconstruction

We address the problem of applying Dual-Energy techniques to Image-Intensifierbased acquisition systems. We propose a new Dual Energy calibration and combination scheme, which takes into account the spatially non-uniform response of the Image Intensifier. During the calibration step, the coefficients of the combination are locally estimated for a set of uniformly spaced points over the image, and interpolated from these samples for the other points. In the combination step, the selective images are generated using the corresponding polynomia for each pixel. 3D selective reconstruction of bone-only structures is presented as a possible application. Experimental results obtained on anthropomorphic phantoms are discussed.