The Measurement of Ventricular Wall Thickening Using Scattered and Fluorescence Radiation

A new technique for the simultaneous measurement of left ventricular epicardial and endocardial wall dynamics is presented. X-rays scattered from the surface of the heart are used to monitor motion at the epicardial surface and iodine fluorescence induced in the blood pool is used to monitor motion of the endocardium. Continuous wall thickness measurements throughout the cardiac cycle are obtained.     

A multiple active contour model for cardiac boundary detection on echocardiographic sequences

Tracing of left-ventricular epicardial and endocardial borders on echocardiographic sequences is essential for quantification of cardiac function. The authors designed a method based on an extension of active contour models to detect both epicardial and endocardial borders on short-axis cardiac sequences spanning the entire cardiac cycle. They validated the results by comparing the computer-generated boundaries to the boundaries manually outlined by four expert observers on 44 clinical data sets. The mean boundary distance between the computer-generated boundaries and the manually outlined boundaries was 2.80 mm (sigma=1.28 mm) for the epicardium and 3.61 (sigma=1.68 mm) for the endocardium. These distances were comparable to interobserver distances, which had a mean of 3.79 mm (sigma=1.53 mm) for epicardial borders and 2.67 mm (sigma=0.88 mm) for endocardial borders. The correlation coefficient between the areas enclosed by the computer-generated boundaries and the average manually outlined boundaries was 0.95 for epicardium and 0.91 for endocardium. The algorithm is fairly insensitive to the choice of the initial curve. Thus, the authors have developed an effective and robust algorithm to extract left-ventricular boundaries from echocardiographic sequences.     

3-D Heart Modeling and Motion Estimation Based on Continuous Distance Transform Neural Networks and Affine Transform

In this paper, we apply the previously proposed continuous distance transform neural network (CDTNN) to represent 3-D endocardial (inner) and epicardial (outer) contours and quantitatively estimate the motion of left ventricles of human hearts from ultrasound images acquired using transesophageal echo-cardiography. This CDTNN has many good properties as the conventional distance transforms, which are suitable for 3-D object representation and deformation estimation. We have successfully represented the 3-D epicardia and endocardia of left ventricles using CDTNNs trained by as few as 7.5% of the manually traced data. The mean absolute error in the testing for one patient over the 27 testing planes were (1.4 ± 1.2 mm) for the endocardium, (1.3 ± 1.0 mm) for the epicardium at end diastole and (1.4 ± 1.2 mm) for the endocardium vs. 1.2 ± 1.0 mm for the epicardium at end systole. The absolute error measured compares favorably with the human inter-observer variability reported for analyzing distances. With this unique distance transform representation that is continuous and differentiable, we are also able to systematically and effectively measure the amount of 3-D heart motion in terms of affine transform.     

Characterization of the Coronary Vascular Capacitance, Resistance, and Flow in Endocardium and Epicardium Based on a Nonlinear Dynamic Analog Model

An electrical analog model consisting of capacitors, diodes, linear, and nonlinear resistors was used to characterize the coronary pressure-flow relationships from the arterial side of the coronary circulation. Based on this analog model, an identifiable system was formulated whereby the coronary vascular capacitance and resistance in the endocardial and epicardial layer of the heart were estimated. This was done by solving a constrained least-squares problem using a nonlinear programming technique. Experimental data were obtained from 28 animal studies using swine with an artificially induced coronary stenosis. The analog model showed a very consistent representation of the coronary hemodynamics. The model also generated accurate estimates of the endocardial to epicardial blood flow ratios compared to those independently measured by the radioactive microsphere technique. The model-predicted epicardial capacitance had a mean of 4.2 ×10-3 mI/mmHg per 100 g tissue; while the endocardial capacitance was negligible in most cases. The result indicated that, in the stenosed coronary circulation of swine, capacitive flow contributes 20 percent in root-mean-square value to the total flow activity in epicardium; while flow in the endocardium is dominated by a resistive, vascular waterfall effect.     

Mapping myocardial fiber orientation using echocardiography-based shear wave imaging

The assessment of disrupted myocardial fiber arrangement may help to understand and diagnose hypertrophic or ischemic cardiomyopathy. We hereby proposed and developed shear wave imaging (SWI), which is an echocardiography-based, noninvasive, real-time, and easy-to-use technique, to map myofiber orientation. Five in vitro porcine and three in vivo open-chest ovine hearts were studied. Known in physics, shear wave propagates faster along than across the fiber direction. SWI is a technique that can generate shear waves travelling in different directions with respect to each myocardial layer. SWI further analyzed the shear wave velocity across the entire left-ventricular (LV) myocardial thickness, ranging between 10 (diastole) and 25 mm (systole), with a resolution of 0.2 mm in the middle segment of the LV anterior wall region. The fiber angle at each myocardial layer was thus estimated by finding the maximum shear wave speed. In the in vitro porcine myocardium (n=5) , the SWI-estimated fiber angles gradually changed from +80° ± 7° (endocardium) to +30° ± 13° (midwall) and -40° ± 10° (epicardium) with 0° aligning with the circumference of the heart. This transmural fiber orientation was well correlated with histology findings. SWI further succeeded in mapping the transmural fiber orientation in three beating ovine hearts in vivo. At midsystole, the average fiber orientation exhibited 71° ± 13° (endocardium), 27° ± 8° (midwall), and -26° ± 30° (epicardium). We demonstrated the capability of SWI in mapping myocardial fiber orientation in vitro and in vivo. SWI may serve as a new tool for the noninvasive characterization of myocardial fiber structure.     

A bioelectric inverse imaging technique based on surface Laplacians

A new approach is proposed to solve bioelectric inverse problems by employing the surface Laplacian of the bioelectrical potential. A theoretical investigation was conducted to test the feasibility of epicardial inverse imaging of cardiac electrical activity. A two-sphere homogeneous volume conductor model, where the inner sphere represents the epicardium and the outer sphere the body surface, was used. Radial and tangential current dipoles were used to approximate localized wavefronts propagating from the endocardium to the epicardium, and ectopic myocardial activities. The epicardial potential distribution was reconstructed from the body surface Laplacians with the aid of the Tikhonov zero-order regularization technique, which then was compared with the results obtained from the body surface potentials using the same regularization scheme. The two inverse solutions were compared qualitatively via visual inspection of the reconstructed epicardial potential maps, and quantitatively by examining relative errors and correlation coefficients between the “true” and the reconstructed epicardial potentials. Both qualitative and quantitative results indicate that the surface Laplacians play a positive role in improving the ill-posed nature of the bioelectric inverse problem, which would enhance one's capability of reconstructing important epicardial events such as extrema in the epicardial potential distribution. The present theoretical study suggests that the Laplacian-based inverse imaging technique may have important applications to epicardial inverse imaging and other bioelectric inverse imaging     

Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection

This paper describes a new fully automatic fuzzy multiresolution-based algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection and tracking on a sequence of short axis (SA) echocardiographic images of a complete cardiac cycle. This is a necessary step for automatic quantification of cardiac function using echo images. The proposed method is a "center-based" approach in which epicardial and endocardial boundary edge points are searched for on radial lines emanating from the LV center point. The central point of the LV cavity is estimated using a fuzzy-based technique in which the "uncertain" spatial, morphological, and intensity information of the image are represented as fuzzy sets and then combined by fuzzy operators. Edge-detection stage uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries over the M radii in the N frames of a complete cardiac cycle. The raw extracted edge points are then processed in the wavelet domain to reduce the effects of noise from the boundaries and papillary muscles from the endocardial boundary extraction process. Finally, a uniform cubic B-spline approximation method is used to define the closed LV boundaries. Experiments with simulated and real echocardiographic images are presented.     

Segmentation of the Ventricle Membranes in Short-Axis Sequences by Optical Flow Base on DLSRE Model

Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiacpapillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.     

Constrained detection of left ventricular boundaries from cine CT images of human hearts

Detection of the left ventricular (LV) endocardial (inner) and epicardial (outer) boundaries in cardiac images, provided by fast computer tomography (cine CT), magnetic resonance (MR), or ultrasound (echocardiography), is addressed. The automatic detection of the LV boundaries is difficult due to background noise, poor contrast, and often unclear differentiation of the tissue characteristics of the ventricles, papillary muscles, and surrounding tissues. An approach to the automatic ventricular boundary detection that employs set-theoretic techniques, and is based on incorporating a priori knowledge of the heart geometry, its brightness, spatial structure, and temporal dynamics into the boundaries detection algorithm is presented. Available knowledge is interpreted as constraint sets in the functional space, and the consistent boundaries are considered to belong to the intersection of all the introduced sets, thus satisfying the a priori information. An algorithm is also suggested for the simultaneous detection of the endocardial and epicardial boundaries of the LV. The procedure is demonstrated using cine CT images of the human heart.     

An ultrasonic method for measuring tissue displacement: technicaldetails and validation for measuring myocardial thickening

We have developed a method for measuring myocardial thickening from a single ultrasonic transducer attached to the epicardium. Displacement of the underlying myocardial tissue is measured by following the phase of the echoes within a sample volume range-gated across the myocardium. The output is in the form of an analog signal. To verify the accuracy, resolution, and limitations of the system, we derived the equations relating the position of a reflector to the phase of its echo and compared the system output in vitro to a known input using a single moving target and a random distribution of scatterers, and in vivo to that of an ultrasonic transit-time dimension gauge. The results demonstrate that the 10 MHz system can accurately follow the motion of single or multiple targets with a resolution of 0.02 mm. In 25 dogs myocardial thickening measured with the displacement system compared favorably in both waveform and magnitude with thickening measured by the two-crystal transit-time method. Applications for the displacement method include: quantification of regional ventricular function in animal models of cardiovascular diseases, measurement of endocardial to epicardial differences in the deformation of regional myocardium during the cardiac cycle, and evaluation of regional cardiac function in patients during and after corrective cardiac surgery.     

Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. Our method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r = 0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p < 0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting.     

Tracking the left ventricle in echocardiographic images by learningheart dynamics

In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented.     

Wall position and thickness estimation from sequences of echocardiographic images

Presents a new method for endocardial (inner) and epicardial (outer) contour estimation from sequences of echocardiographic images. The framework herein introduced is fine-tuned for parasternal short axis views at the papillary muscle level. The underlying model is probabilistic; it captures the relevant features of the image generation physical mechanisms and of the heart morphology. Contour sequences are assumed to be two-dimensional noncausal first-order Markov random processes; each variable has a spatial index and a temporal index. The image pixels are modeled as Rayleigh distributed random variables with means depending on their positions (inside endocardium, between endocardium and pericardium, or outside pericardium). The complete probabilistic model is built under the Bayesian framework. As estimation criterion the maximum a posteriori (MAP) is adopted. To solve the optimization problem, one is led to (joint estimation of contours and distributions' parameters), the authors introduce an algorithm herein named iterative multigrid dynamic programming (IMDP). It is a fully data-driven scheme with no ad-hoc parameters. The method is implemented on an ordinary workstation, leading to computation times compatible with operational use. Experiments with simulated and real images are presented.     

A model-based four-dimensional left ventricular surface detector

The authors have developed a general model-based surface detector for finding the four-dimensional (three spatial dimensions plus time) endocardial and epicardial left ventricular boundaries. The model encoded left ventricular (LV) shape, smoothness, and connectivity into the compatibility coefficients of a relaxation labeling algorithm. This surface detection method was applied to gated single photon emission computed tomography (SPECT) perfusion images, tomographic radionuclide ventriculograms, and cardiac rotation magnetic resonance images. Its accuracy was investigated using actual patient data. Global left ventricular volumes correlated well, with a maximum correlation coefficient of 0.98 for magnetic resonance imaging (MRI) endocardial surfaces and a minimum of 0.88 for SPECT epicardial surfaces. The average absolute errors of edge detection were 6.4, 5.6. and 4.6 mm for tomographic radionuclide ventriculograms, gated perfusion SPECT, and magnetic resonance images, respectively.     

LV shape and motion: B-spline-based deformable model and sequential motion decomposition.

In this paper, we extend a previous work by J. Park and propose a uniform framework to reconstruct left ventricle (LV) geometry/motion from tagged MR images. In our work, the LV is modeled as a generalized prolate spheroid, and its motion is decomposed into four components-global translation, polar radial/z-axis compression, twisting, and bending. By formulating model parameters as tensor products of B-splines, we develop efficient algorithms to quickly reconstruct LV geometry/motion from extracted boundary contours and tracked planar tags. Experiments on both synthesized and in vivo data are also reported.     

Semiautomated border tracking of cine echocardiographic ventnrcular images

Two-dimensional ultrasound sector scans of the left ventricle (LV) are commonly used to diagnose cardiac mechanical function. Present quantification procedures of wall motion by this technique entail inaccuracies, mainly due to relatively poor image quality and the absence of a definition of the relative position of the probe and the heart. The poor quality dictates subjective determination of the myocardial edges, while the absence of a position vector increases the errors in the calculations of wall displacement, LV blood volume, and ejection fraction. An improved procedure is proposed here for automatic myocardial border tracking (AMBT) of the endocardial and epicardial edges in a sequence of video images. The procedure includes nonlinear filtering of whole images, debiasing of gray levels, and location-dependent contrast stretching. The AMBT algorithm is based upon tracking movement of a small number of predefined set of points, which are manually defined on the two myocardial borders. Information from one image is used, by utilizing predetermined statistical criteria to iteratively search and detect the border points on the next one. Border contours are reconstructed by Spline interpolation of the border points. The AMBT procedure is tested by comparing processed sequences of cine echocardiographic scan images to manual tracings by an objective observer and to results from previously published data.     

Segmentation of echocardiographic images using mathematical morphology

A semiautomatic technique for isolating the ventricular endocardial border in echocardiograms from a commercially available two-dimensional phased array ultrasound system is presented. This method processes echo images using mathematical morphology to reduce the effects of range and azimuth variation inherent in echo. After morphological filtering, the endocardial border is extracted with traditional segmentation methods. Further processing of the resulting border using binary morphology produces a region of interest suitable for derivation of motion parameters of the endocardium. The area and the shape of semiautomatically-derived regions correlate well (r>0.93) with those defined by expert observers in a study of induced ischemia in seven canines.<>     

The effect of the cut surface during electrical stimulation of a cardiac wedge preparation

Optical mapping from the cut surface of a "wedge preparation" allows observation inside the heart wall, below the epicardium or endocardium. We use numerical simulations based on the bidomain model to illustrate how the transmembrane potential is influenced by the cut surface. The distribution of transmembrane potential around a unipolar cathode depends on the fiber angle. For intermediate angles, hyperpolarization appears on only one side of the electrode, and is large and widespread.     

Shape-based tracking of left ventricular wall motion

An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a shape-based strategy. Results from the match process are incorporated with a smoothness term into an optimization functional. The global minimum of this functional is found, resulting in a smooth flow field that is consistent with the match data. The computation is performed for all pairs of frames in the temporal sequence and equally sampled points on one contour are tracked throughout the sequence, resulting in a composite flow field over the entire sequence. Two perspectives on characterizing the optimization functional are presented which result in a tradeoff resolved by the confidence in the initial boundary segmentation. Experimental results for contours derived from diagnostic image sequences of three different imaging modalities are presented. A comparison of trajectory estimates with trajectories of gold-standard markers implanted in the LV wall are presented for validation. The results of this comparison confirm that although cardiac motion is a three-dimensional (3-D) problem, two-dimensional (2-D) analysis provides a rich testing ground for algorithm development