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. To improve the pair-wise matching performance of our approach, artificial nodes are introduced to take into account the topology variation between 3D vascular trees. Different similarity measures using tree characteristics and geometric features of coronary branches are evaluated and compared to our previous work.

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. Numerical experiments on different noise densities show that the proposed two algorithms can lead to significantly better results than some typical median type filters. Efficient implementation is also realized via GPU (Graphic Processing Unit)-based parallelization techniques.

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. The estimated parameters of the model are employed to define the boundary points of vessel lumen. The detected boundary points of vessel lumen in six longitudinal images are transformed to the cross-sectional planes in order to calculate the degree of stenosis according to the luminal areas. Our proposed method was evaluated in ten CCTA images with ten reported non-calcified stenosis. The degree of each stenosis was computed according to the luminal area and compared with the standard reference given by radiologists. Experimental results show that our method can estimate the degree of stenosis with a high accuracy.

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. In the first step, down-sampled patient image is registered with a set of low-resolution atlas images. A coarse kidney segmentation result is generated to locate the left and right kidneys. In the second step, the left and right kidneys are cropped from original images and aligned with another set of high-resolution atlas images to obtain the final results respectively. Segmentation results from 14 CT angiographic (CTA) images show that our proposed method can segment the kidneys with a high accuracy. The average Dice similarity coefficient and surface-to-surface distance between segmentation results and reference standard are 0.952 and 0.913mm. Furthermore, the kidney segmentation in CT urography (CTU) and CTA images of 12 patients were performed to show the feasibility of our method in CTU images.

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. A second procedure was applied to refine the position of the path and estimate the local radius along the vessel. This was achieved with an iterative multiscale algorithm based on geometrical moments. Parameter tuning was performed using a dedicated numerical phantom, and then the algorithm was applied to extract the coronary venous system. Results are provided on three MSCT volume sequences acquired for patients selected for a cardiac resynchronization therapy procedure. A visibility study was carried out by a medical expert who labeled venous segments on a set of 18 volumes. A comparison with two other Fast-Marching techniques and a geometrical moment based tracking method is also reported.

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. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.

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. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data.

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. A Compute Unified Device Architecture (CUDA)-based parallelization is applied on the operations of sinusoid fittings and interpolations to accelerate the algorithm. A comparative study is then conducted to evaluate the proposed approach with two other inpainting methods and with a compressed sensing iterative reconstruction. Qualitative and quantitative performances demonstrate that the proposed approach can lead to efficient artifact suppression and less structure blurring.

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. A 3D+t coronary tree model reconstructed from MSCT sequence is employed to enhance the reality of simulation. A simulation environment of X-ray coronary angiography is developed based on distance-driven projection algorithm. The static angiography is firstly simulated to verify the dynamic model by comparing the displacements of landmarks with the real static angiography of the same patient. Rotational simulation results are then obtained with real system parameters to provide a complete and true-to-life RA sequence representing the morphology of moving coronary tree.

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. Simulation results show that the proposed method provides high quality reconstructions with highly sparse sampled noise-free projections. In the presence of noise, the reconstruction quality is still significantly better than the reconstructions obtained with L1-norm or L2-norm priors.

[From medical imaging to image-guided therapy]. / Représentation en sciences du vivant (3). De l'imagerie médicale à la thérapie guidée par l'image.

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.). The last section shows how imaging is involved in therapeutic procedures and in particular the new physical therapies. All along this paper, the research perspectives are sketched.

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.

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. Evaluations are reported on simulated data obtained from a 3D dynamic sequence acquired on a 64-slice GE LightSpeed CT scan. A performance study is conducted to evaluate the quality of the reconstruction of the structures.

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. Finally, a comparison with an existing semi-automatic centreline extraction method is reported. The discussion deals with the clinical usage of such an imaging modality for both global anatomy visualisation and quantification purpose.

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. The transformation functions and correspondence matrices are estimated simultaneously, and allow deformable fitting of the vessels over the volume series. Either point-based or branch-based motion features can be derived. Experiments have been conducted in order to evaluate the performance of the method with a matching error analysis.

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. Performances are comparable to those obtained by reconstruction of a statical coronary tree using an algebraic reconstruction technique algorithm.

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. A level set evolution technique is proposed to yield the deformation between adjacent slices. A modified bilinear interpolation method is then designed to generate propagating image. A multi-resolution scheme is applied to decrease the computation time and support large deformation. The resulting images show good results on regions enclosing different anatomic structures.

Image reconstruction for positron emission tomography using fuzzy nonlinear anisotropic diffusion penalty.

Iterative algorithms such as maximum likelihood-expectation maximization (ML-EM) become the standard for the reconstruction in emission computed tomography. However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations reaches a certain value. In this paper, we have investigated a new iterative algorithm for penalized-likelihood image reconstruction that uses the fuzzy nonlinear anisotropic diffusion (AD) as a penalty function. The proposed algorithm does not suffer from the same problem as that of ML-EM algorithm, and it converges to a low noisy solution even if the iteration number is high. The fuzzy reasoning instead of a nonnegative monotonically decreasing function was used to calculate the diffusion coefficients which control the whole diffusion. Thus, the diffusion strength is controlled by fuzzy rules expressed in a linguistic form. The proposed method makes use of the advantages of fuzzy set theory in dealing with uncertain problems and nonlinear AD techniques in removing the noise as well as preserving the edges. Quantitative analysis shows that the proposed reconstruction algorithm is suitable to produce better reconstructed images when compared with ML-EM, ordered subsets EM (OS-EM), Gaussian-MAP, MRP, TV-EM reconstructed images.

String matching techniques for high-level primitive formation in 2-D vascular imaging.

This paper deals with a so-called "intermediate" description, in other words, the formation of high-level primitives in angiographies. The method is based on an attributed string matching technique capable to capture the shape similarities between low-level primitives (i.e., vessel contours and centerlines). After designing a multiparametric cost function, we propose a multiline pairing algorithm. In order to objectively evaluate its performances, results are first provided on simulated data and then on a set of coronarographic images, where it is shown that anatomically coherent entities like vessel segments and branches can be built, "objects" that can be further individually analyzed for clinical purpose.