Three-dimensional modeling for functional analysis of cardiac images: a review

Deformable biomechanical models: Application to 4D cardiac image analysis

This article describes a methodology for creating a generic volumetric biomechanical model from different image modalities and segmenting time series of medical images using this model. The construction of such a generic model consists of three stages: geometric meshing, non-rigid deformation of the mesh in images of various modalities, and image-to-mesh information mapping through rasterization. The non-rigid deformation stage, which relies on a combination of global and local deformations, can then be used to segment time series of images, e.g. cine MRI or gated SPECT cardiac images. We believe that this type of deformable biomechanical model can play an important role in the extraction of useful quantitative local parameters of cardiac function. The biomechanical model of the heart will be coupled with an electrical model of another collaborative project in order to simulate and analyze a larger class of pathologies.

Stochastic finite element framework for simultaneous estimation of cardiac kinematic functions and material parameters

A stochastic finite element framework is presented for the simultaneous estimation of the cardiac kinematic functions and material model parameters from periodic medical image sequences. While existing biomechanics studies of the myocardial material constitutive laws have assumed known tissue kinematic measurements, and image analysis efforts on cardiac kinematic functions have relied on fixed constraining models of mathematical or mechanical nature, we illustrate through synthetic data that a probabilistic joint estimation strategy is needed to achieve more robust and accurate analysis of the kinematic functions and material parameters at the same time. For a particular a priori constraining material model with uncertain subject-dependent parameters and a posteriori noisy imaging based observations, our strategy combines the stochastic differential equations of the myocardial dynamics with the finite element method, and the material parameters and the imaging data are treated as random variables with known prior statistics. After the conversion to state space representation, the extended Kalman filtering procedures are adopted to linearize the equations and to provide the joint estimates in an approximate optimal sense. The estimation bias and convergence issues are addressed, and we conclude experimentally that it is possible to adopt this biomechanical model based multiframe estimation approach to achieve converged estimates because of the periodic nature of the cardiac dynamics. The effort is validated using synthetic data sequence with known kinematics and material parameters. Further, under linear elastic material model, estimation results using canine magnetic resonance phase contrast image sequences are presented, which are in very good agreement with histological tissue staining results, the current gold standards.

FAME--a flexible appearance modeling environment

Combined modeling of pixel intensities and shape has proven to be a very robust and widely applicable approach to interpret images. As such the active appearance model (AAM) framework has been applied to a wide variety of problems within medical image analysis. This paper summarizes AAM applications within medicine and describes a public domain implementation, namely the flexible appearance modeling environment (FAME). We give guidelines for the use of this research platform, and show that the optimization techniques used renders it applicable to interactive medical applications. To increase performance and make models generalize better, we apply parallel analysis to obtain automatic and objective model truncation. Further, two different AAM training methods are compared along with a reference case study carried out on cross-sectional short-axis cardiac magnetic resonance images and face images. Source code and annotated data sets needed to reproduce the results are put in the public domain for further investigation.

A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion

In this paper, a new approach is presented for the assessment of a 3-D anatomical and functional model of the heart including structural information from magnetic resonance imaging (MRI) and functional information from positron emission tomography (PET) and magnetocardiography (MCG). The method uses model-based co-registration of MR and PET images and marker-based registration for MRI and MCG. Model-based segmentation of MR anatomical images results in an individualized 3-D biventricular model of the heart including functional parameters from PET and MCG in an easily interpretable 3-D form.

Automated cardiac MR image segmentation: theory and measurement evaluation

We present a new approach to magnetic resonance image segmentation with a Gradient-Vector-Flow-based snake applied to selective smoothing filtered images. The system also allows automated image segmentation in the presence of grey scale inhomogeneity, as in cardiac Magnetic Resonance imaging. Removal of such inhomogeneities is a difficult task, but we proved that using non-linear anisotropic diffusion filtering, myocardium edges are selectively preserved. The approach allowed medical data to be automatically segmented in order to track not only endocardium, which is usually a less difficult task, but also epicardium in anatomic and perfusion studies with Magnetic Resonance. The method developed proceeds in three distinct phases: (a) an anisotropic diffusion filtering tool is used to reduce grey scale inhomogeneity and to selectively preserve edges; (b) a Gradient-Vector-Flow-based snake is applied on filtered images to allow capturing a snake from a long range and to move into concave boundary regions; and (c) an automatic procedure based on a snake is used to fit both endocardium and epicardium borders in a multiphase, multislice examination. A good agreement (P<0.001) between manual and automatic data analysis, based on the mean difference+/-SD, was assessed in a pool of 907 cardiac function and perfusion images.

On the optimality of magnetic resonance tag patterns for heart wall motion estimation

Tracking of cardiac motion using magnetic resonance tagging has attracted increasing attention in recent years. Several methods for tagging the cardiac tissue and tracking the motion of the tags have been developed. However, the choice of tag pattern that minimizes tracking error has received less attention. In this paper, we are concerned with the optimal tagging and acquisition of MR tagged images for cardiac motion analysis. We formulate the measurement of tissue deformation as a multidimensional parametric estimation problem which can be solved using the nonlinear least squares estimator. Along with this, we derive the Cramer-Rao lower bound (CRLB) on the average estimation error variance. We then show that under certain conditions a complex sinusoidal tag shape minimizes the CRLB. We validate our results with computer simulations. Finally, based on the previous findings, we make recommendations concerning the most desirable imaging strategy for images tagged with a complex sinusoidal tag pattern.

Automated 3-D echocardiography analysis compared with manual delineations and SPECT MUGA

A major barrier for using 3-D echocardiography for quantitative analysis of heart function in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis automatic. In this paper, we present an automated three-dimensional (3-D) echocardiographic acquisition and image-processing methodology for assessment of left ventricular (LV) function. We combine global image information provided by a novel multiscale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We then use the segmentation results to fit and track the LV endocardial surface using a 3-D continuous transformation. To our knowledge, this is the first report of a completely automated method. The protocol is evaluated in a small clinical case study (nine patients). We compare ejection fractions (EFs) computed with the new approach to those obtained using the standard clinical technique, single-photon emission computed tomography multigated acquisition. Errors on six datasets were found to be within six percentage points. A further two, with poor image quality, improved upon EFs from manually delineated contours, and the last failed due to artifacts in the data. Volume-time curves were derived and the results compared to those from manual segmentation. Improvement over an earlier published version of the method is noted.

Integrated surface model optimization for freehand three-dimensional echocardiography

The major obstacle of three-dimensional (3-D) echocardiography is that the ultrasound image quality is too low to reliably detect features locally. Almost all available surface-finding algorithms depend on decent quality boundaries to get satisfactory surface models. We formulate the surface model optimization problem in a Bayesian framework, such that the inference made about a surface model is based on the integration of both the low-level image evidence and the high-level prior shape knowledge through a pixel class prediction mechanism. We model the probability of pixel classes instead of making explicit decisions about them. Therefore, we avoid the unreliable edge detection or image segmentation problem and the pixel correspondence problem. An optimal surface model best explains the observed images such that the posterior probability of the surface model for the observed images is maximized. The pixel feature vector as the image evidence includes several parameters such as the smoothed grayscale value and the minimal second directional derivative. Statistically, we describe the feature vector by the pixel appearance probability model obtained by a nonparametric optimal quantization technique. Qualitatively, we display the imaging plane intersections of the optimized surface models together with those of the ground-truth surfaces reconstructed from manual delineations. Quantitatively, we measure the projection distance error between the optimized and the ground-truth surfaces. In our experiment, we use 20 studies to obtain the probability models offline. The prior shape knowledge is represented by a catalog of 86 left ventricle surface models. In another set of 25 test studies, the average epicardial and endocardial surface projection distance errors are 3.2 +/- 0.85 mm and 2.6 +/- 0.78 mm, respectively.

3-D active appearance models: segmentation of cardiac MR and ultrasound images

A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R2 = 0.94, 0.97, 0.82, respectively. For echocardiographic analysis, the area correlation was R2 = 0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting.

Left ventricular volume estimation for real-time three-dimensional echocardiography

The application of level set techniques to echocardiographic data is presented. This method allows semiautomatic segmentation of heart chambers, which regularizes the shapes and improves edge fidelity, especially in the presence of gaps, as is common in ultrasound data. The task of the study was to reconstruct left ventricular shape and to evaluate left ventricular volume. Data were acquired with a real-time three-dimensional (3-D) echocardiographic system. The method was applied directly in the three-dimensional domain and was based on a geometric-driven scheme. The numerical scheme for solving the proposed partial differential equation is borrowed from numerical methods for conservation law. Results refer to in vitro and human in vivo acquired 3-D + time echocardiographic data. Quantitative validation was performed on in vitro balloon phantoms. Clinical application of this segmentation technique is reported for 20 patient cases providing measures of left ventricular volumes and ejection fraction.

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.

Efficient model-based quantification of left ventricular function in 3-D echocardiography

Quantitative functional analysis of the left ventricle plays a very important role in the diagnosis of heart diseases. While in standard two-dimensional echocardiography this quantification is limited to rather crude volume estimation, three-dimensional (3-D) echocardiography not only significantly improves its accuracy but also makes it possible to derive valuable additional information, like various wall-motion measurements. In this paper, we present a new efficient method for the functional evaluation of the left ventricle from 3-D echographic sequences. It comprises a segmentation step that is based on the integration of 3-D deformable surfaces and a four-dimensional statistical heart motion model. The segmentation results in an accurate 3-D + time left ventricle discrete representation. Functional descriptors like local wall-motion indexes are automatically derived from this representation. The method has been successfully tested both on electrocardiography-gated and real-time 3-D data. It has proven to be fast, accurate, and robust.

A review of cardiac image registration methods

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

Reconstruction of time-varying 3-D left-ventricular shape from multiview X-ray cineangiocardiograms

This paper reports on the clinical application of a system for recovering the time-varying three-dimensional (3-D) left-ventricular (LV) shape from multiview X-ray cineangiocardiograms. Considering that X-ray cineangiocardiography is still commonly employed in clinical cardiology and computational costs for 3-D recovery and visualization are rapidly decreasing, it is meaningful to develop a clinically applicable system for 3-D LV shape recovery from X-ray cineangiocardiograms. The system is based on a previously reported closed-surface method of shape recovery from two-dimensional occluding contours with multiple views. To apply the method to "real" LV cineangiocardiograms, user-interactive systems were implemented for preprocessing, including detection of LV contours, calibration of the imaging geometry, and setting of the LV model coordinate system. The results for three real LV angiographic image sequences are presented, two with fixed multiple views (using supplementary angiography) and one with rotating views. 3-D reconstructions utilizing different numbers of views were compared and evaluated in terms of contours manually traced by an experienced radiologist. The performance of the preprocesses was also evaluated, and the effects of variations in user-specified parameters on the final 3-D reconstruction results were shown to be sufficiently small. These experimental results demonstrate the potential usefulness of combining multiple views for 3-D recovery from "real" LV cineangiocardiograms.

Left ventricular 4D echocardiogram motion and shape analysis

The article describes the methodology and the processes of modeling the function and the motion of left ventricle using transesophageal echocardiograph. The parameters can be used in studying the functionality of left ventricle, the status of abnormality of myocardial, and the geometric and morphological of left ventricle in shape analysis. The parameters describes the motion of left ventricle are the left ventricular (LV) floating long axis, the morphological parameters. The LV morphological parameters describe the wall motion, the LV chamber cavity variation, the effective R-ratio of endomyocardial chamber of LV, the area surface curvature, and the global surface curvature circularity. The parameters such as stroke volume, ejection fraction used in evaluation of LV functions are also extracted.

Conversion of left ventricular endocardial positions from patient-independent co-ordinates into biplane fluoroscopic projections

Electrocardiographic body surface mapping is used clinically to guide catheter ablation of cardiac arrhythmias by providing an estimate of the site of origin of an arrhythmia. The localisation methods used in our group produce results in left-ventricular cylinder co-ordinates (LVCCs), which are patient-independent but hard to interpret during catheterisation in the electrophysiology laboratory. It is preferable to provide these results as three-dimensional (3D) co-ordinates which can be presented as projections in the biplane fluoroscopic views that are used routinely to monitor the catheter position. Investigations were carried out into how well LVCCs can be converted into fluoroscopic projections with the limited anatomical data available in contemporary clinical practice. Endocardial surfaces from magnetic resonance imaging (MRI) scans of 24 healthy volunteers were used to create an appropriate model of the left-ventricular endocardial wall. Methods for estimation of model parameters from biplane fluoroscopic images were evaluated using simulated biplane data created from these surfaces. In addition, the conversion method was evaluated, using 107 catheter positions obtained from eight patients, by computing LVCCs from biplane fluoroscopic images and reconstructing the 3D positions using the model. The median 3D distance between reconstructed positions and measured positions was 4.3mm.