Inexact coronary tree matching algorithm with artificial nodes.

Coronary tree matching is applied to plan percutaneous vascular procedures. This work, which allows following each segment of non-isomorphic coronary trees over time, precedes the determination of the best 2D angiography view from C-arm acquisition system for angioplasty procedure. To match two 3D coronary trees which represent two successive cardiac phases, we adapted a reference inexact tree matching algorithm based on association graph and maximum clique.

Automatic kidney segmentation in CT images based on multi-atlas image registration.

Kidney segmentation is an important step for computer-aided diagnosis or treatment in urology. In this paper, we present an automatic method based on multi-atlas image registration for kidney segmentation. The method mainly relies on a two-step framework to obtain coarse-to-fine segmentation results.

Quantitative measurement of coronary artery stenosis in CCTA images using a 2D parametric intensity model.

In this paper, we propose an approach based on 2D vessel model to segment the vessel lumen in three-dimensional coronary computed tomographic angiography (CCTA) images. The 2D parametric intensity model is introduced first to simulate the intensity distribution of vessel lumen with different size in the longitudinal images. Then the Levenberg-Marquardt method is applied to fit the model within a series of region-of interests defined in the longitudinal image.

2-D impulse noise suppression by recursive gaussian maximum likelihood estimation.

An effective approach termed Recursive Gaussian Maximum Likelihood Estimation (RGMLE) is developed in this paper to suppress 2-D impulse noise. And two algorithms termed RGMLE-C and RGMLE-CS are derived by using spatially-adaptive variances, which are respectively estimated based on certainty and joint certainty & similarity information. To give reliable implementation of RGMLE-C and RGMLE-CS algorithms, a novel recursion stopping strategy is proposed by evaluating the estimation error of uncorrupted pixels.

Coronary vein extraction in MSCT volumes using minimum cost path and geometrical moments.

This work deals with the extraction of patient-specific coronary venous anatomy in preoperative multislice computed tomography (MSCT) volumes. A hybrid approach has been specifically designed for low-contrast vascular structure detection. It makes use of a minimum cost path technique with a Fast-Marching front propagation to extract the vessel centerline.

Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering.

Reducing patient radiation dose, while maintaining a high-quality image, is a major challenge in Computed Tomography (CT). The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non linear processing is first applied to suppress the noise and artifacts, that is based on a sparsity prior in term of a learned dictionary, then an unsharp filtering aiming to enhance the contrast of tissues and compensate the contrast loss caused by the DL processing. Preliminary results show that the proposed method is effective in suppressing mottled noise as well as improving tumor detectability.

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing.

Simulation environment of X-ray rotational angiography using 3D+t coronary tree model.

The newly introduced cardiac rotational angiography (RA) can provide a large amount of projections from different angles which greatly improve the 3D coronary tree reconstruction. However, the reconstruction methods are difficult to be objectively evaluated due to the complicated topology of coronary tree and non-linear cardiac motion. In this paper, we present a simulation environment of rotational angiography acquisition system to facilitate the improvements and the evaluations of reconstruction algorithms.

Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations.

The anatomy and motion of the heart and the aorta are essential for patient-specific simulations of cardiac electrophysiology, wall mechanics and hemodynamics. Within the European integrated project euHeart, algorithms have been developed that allow to efficiently generate patient-specific anatomical models from medical images from multiple imaging modalities. These models, for instance, account for myocardial deformation, cardiac wall motion, and patient-specific tissue information like myocardial scar location.

Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means.

The x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues.

Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation.

Projection incompleteness in x-ray computed tomography (CT) often relates to sparse sampling or detector gaps and leads to degraded reconstructions with severe streak and ring artifacts. To suppress these artifacts, this study develops a new sinogram inpainting strategy based on sinusoid-like curve decomposition and eigenvector-guided interpolation, where each missing sinogram point is considered located within a group of sinusoid-like curves and estimated from eigenvector-guided interpolation to preserve the sinogram texture continuity. The proposed approach is evaluated on real two-dimensional fan-beam CT data, for which the projection incompleteness, due to sparse sampling and symmetric detector gaps, is simulated.

L0 constrained sparse reconstruction for multi-slice helical CT reconstruction.

In this paper, we present a Bayesian maximum a posteriori method for multi-slice helical CT reconstruction based on an L0-norm prior. It makes use of a very low number of projections. A set of surrogate potential functions is used to successively approximate the L0-norm function while generating the prior and to accelerate the convergence speed.

[From medical imaging to image-guided therapy].

This survey on medical imaging provides a look into three major components. The first one deals with the full steps through which it must be apprehended: from the sensors to the reconstruction, from the image analysis up to its interpretation. The second aspect describes the physical principles used for imaging (magnetic resonance, acoustic, optics, etc.

A qualitative and quantitative study of coronary artery MRA.

In this paper, we propose an analysis of the coronary arterial tree obtained through magnetic resonance angiography (MRA). Ten datasets of the state-of-the-art SSFP MRI sequence are first qualitatively evaluated and labelled. Second, a quantitative analysis of anatomical and image features is performed.

ECG gated tomographic reconstruction for 3-D rotational coronary angiography.

A method is proposed for 3-D reconstruction of coronary from a limited number of projections in rotational angiography. A Bayesian maximum a posteriori (MAP) estimation is applied with a Poisson distributed projection to reconstruct the 3D coronary tree at a given instant of the cardiac cycle. Several regularizers are investigated L0-norm, L1 and L2 -norm in order to take into account the sparsity of the data.

A model-based reconstruction method for 3-D rotational coronary angiography.

This paper presents a model-based reconstruction method of the coronary tree from a few number of projections in rotational angiography imaging. The reconstruction relies on projections acquired at a same cardiac phase and an energy function minimization that aims to lead the deformation of the 3D model to fit projection data whereas preserving coherence both in time and space. Some preliminary results are provided on simulated rotational angiograms.

Characterization of 3-D coronary tree motion from MSCT angiography.

This paper describes a method for the characterization of coronary artery motion using multislice computed tomography (MSCT) volume sequences. Coronary trees are first extracted by a spatial vessel tracking method in each volume of MSCT sequence. A point-based matching algorithm, with feature landmarks constraint, is then applied to match the 3-D extracted centerlines between two consecutive instants over a complete cardiac cycle.

Motion compensated tomography reconstruction of coronary arteries in rotational angiography.

This paper deals with the 3-D reconstruction of the coronary tree from a rotational X-ray projection sequence. It describes the following three stages: the reconstruction of the 3-D coronary tree at different phases of the cardiac cycle, the motion estimation, and the motion-compensated tomographic reconstruction of the 3-D coronary tree at one given phase using all the available projections. Our method is tested on a series of simulated images computed from the projection of a segmented dynamic volume sequence acquired in multislice computed tomography imaging.

An improved spatial tracking algorithm applied to coronary veins into Cardiac Multi-Slice Computed Tomography volume.

This paper describes an enhanced vessel tracking algorithm. The method specifity relies on the coronary venous tree extraction through Cardiac Multi-Slice Computed Tomography (MSCT). Indeed, contrast inhomogeneities are a major issue in the data sets that necessitate a robust tracking procedure.

Volume reconstruction based on non-rigid registration.

Volume reconstruction is one of the key problems in 3D image rendering and analysis. Inter slice interpolation methods have been widely discussed in the literature and object-based algorithms have been shown to well behave. In this paper, we present a non-rigid registration based strategy to improve the volume reconstruction.

Temporal tracking of coronaries in multi-slice computed tomography.

A method is proposed that performs a temporal tracking of the coronaries in multi-slice computed tomography (MSCT) dynamic sequences. The process exploits geometric moments and a local cylindrical approximation of the vessel to estimate the local characteristics of the structure in each volume and estimate its displacement along the sequence. The research strategy is based on a region matching process to find the location of the point in the successive volumes.

Simulation environment for the evaluation of 3D coronary tree reconstruction algorithms in rotational angiography.

We present a preliminary version of a simulation environment to evaluate the 3D reconstruction algorithms of the coronary arteries in rotational angiography. It includes the construction of a 3D dynamic model of the coronary tree from patient data, the modeling of the rotational angiography acquisition system to simulate different acquisition and gating strategies and the calculation of radiographic projections of the 3D model of coronary tree throughout several cardiac cycles.

A multiscale tracking algorithm for the coronary extraction in MSCT angiography.

This paper deals with the extraction of the coronary network on dynamic volume sequences, acquired in multi-slice spiral computed tomography (MSCT). The proposed approach makes use of a tracking algorithm of the vascular structure, combining a 3D geometric moment operator with a multiscale Hessian filter to estimate the vessel central axis location, its local diameter and orientation. The method performs at the same time, a bifurcation detection to reconstitute the structure of the coronary network.

Temporal tracking of coronaries in MSCTA by means of 3D geometrical moments.

An algorithm is proposed that perform a temporal tracking of the vessel central axis in a 3-D dynamic sequence in multi-slice computed tomography (MSCT). The approach is based on geometric moments and a local cylindrical approximation. The local characteristics of the vessel are estimated on the first volume of the sequence (position on the vessel central axis, local diameter, intravascular and background intensities), then used to track the vessel along the sequence.

Vessel extraction in coronary X-ray Angiography.

This paper describes a method to extract the vascular centerlines and contours in coronary angiography. The proposed approach associates geometric moments for the estimation of a "cylinder-like model" and relies on a tracking process. The orientation of the cylinder axis and its local diameter are computed from the analytical expressions of the geometric moments of up to order 2.