Catálogo de publicaciones - libros

Over the last decade, many methods for adaptively filtering a data stream have been proposed. Those methods have applications in two dimensional imaging as well as in three dimensional image reconstruction. Although the primary objective of this filtering technique is to reduce the noise while avoiding to blur the edges, diagnostic, automated segmentation and surgery show a growing interest in enhancing the features contained in the image flow.

Most of the methods proposed so far emerged from thorough studies of the physics of the considered modality and therefore show only a marginal capability to be extended across modalities. Moreover, adaptive filtering belongs to the family of processing intensive algorithms. Existing technology has often driven to simplifi-cations and modality specific optimization to sustain the expected performances.

In the specific case of real time digital X-ray as used surgery, the system has to sustain a throughput of 30 frames per second.

In this study, we take a generalized approach for adaptive filtering based on multiple oriented filters. Mapping the filtering part to the embedded real time image processing while a user/application defined adaptive recombination of the filter outputs allow to change the smoothing and edge enhancement properties of the filter without changing the oriented filter parameters.

We have implemented the filtering on a Cell Broadband Engine processor and the adaptive recombination on an off-the-shelf PC, connected via Gigabit Ethernet. This implementation is capable of filtering images of 5122 pixels at a throughput in excess of 40 frames per second while allowing to change the parameters in real time.

In this paper we present an approach for extracting patient individual volume conductor models (VCM) using volume data acquired from Magnetic Resonance Imaging (MRI) for computational biology of electrical excitation in the patient’s heart. The VCM consists of the compartments chest surface, lung surfaces, the atrial and ventricular myocardium, and the blood masses. For each compartment a segmentation approach with no or little necessity of user interaction was implemented and integrated into a VCM segmentation pipeline to enable the inverse problem of electrocardiography to become clinical applicable. The segmentation pipeline was tested using volume data from ten patients with structurally normal hearts.

We are working to develop beating-heart atrial septal defect (ASD) closure techniques using real-time 3-D ultrasound guidance. The major image processing challenges are the low image quality and the high frame rate. This paper presents comparative results for ASD tracking in sequences of 3D cardiac ultrasound. We introduce a block flow technique, which combines the velocity computation from optical flow for an entire block with template matching. Enforcing similarity constraints to both the previous and first frames ensures optimal and unique solutions. We compare the performance of the proposed algorithm with that of block matching and optical flow on six in-vivo 4D datasets acquired from porcine beating-heart procedures. Results show that our technique is more stable and has higher sensitivity than both optical flow and block matching in tracking ASDs. Computing velocity at the block level, our technique is much faster than optical flow and comparable in computation cost to block matching.

Complex electrophysiology (EP) procedures, such as catheter-based ablation in the left atrium and pulmonary veins (LAPV) for treatment of atrial fibrillation, require knowledge of heart chamber anatomy. Electroanatomical mapping (EAM) is typically used to define cardiac structures by combining electromagnetic spatial catheter localization with surface models which interpolate the anatomy between EAM point locations in 3D. Recently, the incorporation of pre-operative volumetric CT or MR data sets has allowed for more detailed maps of LAPV anatomy to be used intra-operatively. Preoperative data sets are however a rough guide since they can be acquired several days to weeks prior to EP intervention. Due to positional and physiological changes, the intra-operative cardiac anatomy can be different from that depicted in the pre-operative data.

We present a novel application of contrast-enhanced rotational X-ray imaging for CT-like reconstruction of 3D LAPV anatomy during the intervention itself. We perform two selective contrast-enhanced rotational acquisitions and reconstruct CT-like volumes with 3D filtered back projection. Two volumes depicting the left and right portions of the LAPV are registered and fused. The combined data sets are then visualized and segmented intra-procedurally to provide anatomical data and surface models for intervention guidance. Our results from animal and human experiments indicate that the anatomical information from intra-operative CT-like reconstructions compares favorably with pre-acquired CT data and can be of sufficient quality for intra-operative guidance.

Real time three-dimensional echocardiography opens the possibility of interactive, fast three-dimensional analysis of cardiac anatomy and function. However, at the present time these capabilities cannot be fully exploited due to the low image quality associated to this modality. We propose to increase image quality and information content by combining images acquired from different echocardiographic windows. In this paper, we present an algorithm to register these datasets. Phase-based measures have been proposed as a suitable alternative to intensity-based ones for ultrasound image analysis. The proposed algorithm uses a new cost function, based on local orientation and phase differences, to align the datasets. Visual observation of results, and preliminary numerical analysis, show the robustness and accuracy of this method.

This paper presents an outlier immune 3D active shape models framework for robust volumetric segmentation of the carotid artery required for accurate plaque burden assessment. In the proposed technique, outlier handling is based on a shape metric that is invariant to scaling, rotation and translation by using the ratio of inter-landmark distances as a local shape dissimilarity measure. Tolerance intervals for each descriptor are calculated from the training samples and used to infer the validity of landmarks. The identified outliers are corrected prior to the model fitting using the ratios distributions and appearance information. To improve the feature point search, the method exploits the geometrical knowledge from the outlier analysis at the previous iteration to weight the gray level appearance based fitness measure. A combined intensity-phase feature point search is also introduced which significantly limits the presence of outliers and improves the overall search accuracy. Both numerical and assessments of the method involving volumetric segmentation of the carotid artery have shown that the outlier handling technique is capable of handling a significant presence of outliers independently of the amplitudes.

The passive material properties of myocardium are important in the understanding of diastolic cardiac dysfunction. We determined hyperelastic myocardial material parameters in four isolated arrested pig hearts undergoing passive inflation of the left ventricle. Using geometry from MRI, recorded boundary conditions, muscle fiber architecture from diffusion tensor imaging, and deformation from tissue tagging, finite element models were constructed to solve the finite elasticity stress estimation problem. The constitutive parameters of a hyperelastic transversely isotropic material law were determined by minimizing the difference between the predicted and imaged deformation field. The optimized parameters were in a similar range as those reported by previous studies, showing increased passive stiffness in the muscle fiber direction. The average RMS error was 0.92 mm, similar to the image resolution of 0.80 mm. Optimization of hyperelastic models of myocardial mechanics can thus be performed to extract meaningful biophysical parameters from MRI data.

In this paper we present an accurate cardiac boundary tracking method for 2D tagged MRI time sequences. This method naturally integrates the motion and the static local appearance features and generates accurate boundary criteria via a boosting approach. We extend the conventional Adaboost classifier into a posterior probability form, which can be embedded in a particle filtering-based shape tracking framework. To make the tracking process more robust and faster, we use a PCA subspace shape representation to constrain the shape variation and lower the dimensionality. We also learn two shape-dynamic models for systole and diastole separately, to predict the shape evolution. Our tracking method incorporates the static appearance, the motion appearance, the shape constraints, and the dynamic prediction in a unified way. The proposed method has been implemented on 50 tagged MRI sequences. The experimental results show the accuracy and robustness of our approach.

Accurate retinal blood vessel detection offers a great opportunity to predict and detect the stages of various ocular and systemic diseases, such as glaucoma, hypertension and congestive heart failure, since the change in width of blood vessels in retina has been reported as an independent and significant prospective risk factor for such diseases. In large-population studies of disease control and prevention, there exists an overwhelming need for an automatic tool that can reliably and accurately identify and measure retinal vessel diameters. To address requirements in this clinical setting, a vessel detection algorithm is proposed to quantitatively measure the salient properties of retinal vessel and combine the measurements by Bayesian decision to generate a confidence value for each detected vessel segment. The salient properties of vessels provide an alternative approach for retinal vessel detection at a level higher than detection at the pixel level. Experiments show superior detection performance than currently published results using a publicly available data set. More importantly, the proposed algorithm provides the confidence measurement that can be used as an objective criterion to select reliable vessel segments for diameter measurement.

We have derived and evaluated parameters from ultrasound images of the neck to permit a computer to automatically characterize and differentiate between the carotid artery and jugular vein at image acquisition time during vascular interventions, given manually placed seed points. Our goal is to prevent inadvertent damage to the carotid artery when targeting the jugular vein for catheterization. We used a portable 10 MHz ultrasound system to acquire cross sectional B-mode ultrasound images of these great vessels at 10 fps. An expert user identified the vessels in the first frame by touching the vessels on the screen with his fingertip, and the computer automatically tracked the vessels and calculated a best-fit ellipse for each vessel in each subsequent frame. Vessel location and radii were further analyzed to produce parameters that proved useful for differentiating between the carotid artery and jugular vein. These parameters include relative location of the vessels, distension of the vessel walls, and consistent phase difference between the arterial and venous pulsations as determined by temporal Fourier analysis.