Assessment of a model for overall left ventricular three-dimensional motion from MRI data

Multiple two-dimensional active shape model framework for right ventricular segmentation

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model. The proposed method was tested using cardiac MRI images from 56 human subjects. Compared to a golden reference of manually delineated RV contours, the proposed method resulted in significantly lower error than (almost one half) that of the conventional 2D ASM especially at the apical slices. The mean absolute distance of the proposed method was 2.9 ± 2 mm while the conventional 2D ASM resulted in an error of 6.6 ± 4.5 mm. In addition, the computation time of the proposed method was 5 s compared to 4 ± 1 min previously reported for the 3D ASM formulation.

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.

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

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: 1) to serve as a tutorial of the field for both clinicians and technologists, 2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and 3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.

Tracking geometrical descriptors on 3-D deformable surfaces: application to the left-ventricular surface of the heart

Motion and deformation analysis of the myocardium are of utmost interest in cardiac imaging. Part of the, research is devoted to the estimation of the heart function by analysis of the shape changes of the left-ventricular endocardial surface. However, most clinically used shape-based approaches are often two-dimensional (2-D) and based on the analysis of the shape at only two cardiac instants. Three-dimensional (3-D) approaches generally make restrictive hypothesis about the actual endocardium motion to be able to recover a point-to-point correspondence between two surfaces. The present work is a first step toward the automatic spatio-temporal analysis and recognition of deformable surfaces. A curvature-based and easily interpretable description of the surfaces is derived. Based on this description, shape dynamics is first globally estimated through the temporal shape spectra. Second, a regional curvature-based tracking approach is proposed assuming a smooth deformation. It combines geometrical and spatial information in order to analyze a specific endocardial region. These methods are applied both on true 3-D X-ray data and on simulated normal and abnormal left ventricles. The results are coherent and easily interpretable. Shape dynamics estimations and comparisons between deformable object sequences are now possible through these techniques. This promising framework is a suitable tool for a complete regional description of deformable surfaces.

Estimation of three-dimensional cardiac velocity fields: assessment of a differential method and application to three-dimensional CT data

We have investigated an optical flow method for the estimation of the three-dimensional endocardial wall motion from high-resolution X-ray CT data. This method was originally proposed by Song and Leahy. It is based on the optical flow, the divergence-free and the smoothness constraints. Due to the characteristics of the imaging modality, we studied the restriction of this approach to the boundary of the left ventricular (LV) cavity. The behaviour of the method is quantified through simulations approximating the overall motion of the LV cavity through an affine transform involving a dilation and a rotation. The method implies the choice of three parameters weighting the constraints. The results show a weak dependence of the velocity field on the weighting of the optical flow constraint. The accuracy of the method relies more heavily on the relative weighting of the smoothness and divergence-free constraints. In our experiments, the best results were obtained for a largely predominant divergence-free constraint. The results also show that the accuracy of the method is reasonable for low values of the rotation angle (minimum mean error of 1.1 voxel for 5 degrees). This is compatible with values reported in other studies for the overall rotation of the LV. We provide a qualitative description of the results obtained in vivo on a canine heart by visualizing the distribution of the estimated velocity vector magnitudes over the endocardial surface. These results (evolution of the field over time, maximum velocities) are in agreement with the known physiological behaviour of the heart.

Analysis of left ventricular wall motion based on volumetric deformable models and MRI-SPAMM

We present a new approach for the analysis of the left ventricular shape and motion based on the development of a new class of volumetric deformable models. We estimate the deformation and complex motion of the left ventricle (LV) in terms of a few parameters that are functions and whose values vary locally across the LV. These parameters capture the radial and longitudinal contraction, the axial twisting, and the long-axis deformation. Using Lagrangian dynamics and finite-element theory, we convert these volumetric primitives into dynamic models that deform due to forces exerted by the datapoints. We present experiments where we used magnetic tagging (MRI-SPAMM) to acquire datapoints from the LV during systole. By applying our method to MRI-SPAMM datapoints, we were able to characterize the 3-D shape and motion of the LV both locally and globally, in a clinically useful way. In addition, based on the model parameters we were able to extract quantitative differences between normal and abnormal hearts and visualize them in a way that is useful to physicians.

Deformable models with parameter functions for cardiac motion analysis from tagged MRI data

The authors present a new method for analyzing the motion of the heart's left ventricle (LV) from tagged magnetic resonance imaging (MRI) data. Their technique is based on the development of a new class of physics-based deformable models whose parameters are functions. They allow the definition of new parameterized primitives and parameterized deformations which can capture the local shape variation of a complex object. Furthermore, these parameters are intuitive and require no complex post-processing in order to be used by a physician. Using a physics-based approach, the authors convert the geometric models into dynamic models that deform due to forces exerted from the datapoints and conform to the given dataset. The authors present experiments involving the extraction of the shape and motion of the LV's mid-wall during systole from tagged MRI data based on a few parameter functions. Furthermore, by plotting the variations over time of the extracted LV model parameters from normal and abnormal heart data along the long axis, the authors are able to quantitatively characterize their differences.

3D functional mapping of left ventricular dynamics

The heart is an organ which functions by a periodic change of the three dimensional (3D) spatially distributed parameters; malfunctions of the heart's operating systems are manifested by changes of the spatio-temporal heart shape dynamics. A comprehensive quantitative study of this dynamic shape-function relationship is restricted by the partial character of the available data sets obtained by conventional imaging technologies and by limitations of the image analysis tools. This paper attempts to present a set of image analysis tools aimed at a thorough study of the left ventricular (LV) shape-function relationship based on Cine CT data. Data processing methodologies aimed at analysis and interpretation of the dynamic 3D LV shape, thickening and motion are described. These include the computerized detection of the LV boundaries, dynamic reconstruction of 3D LV shape, the LV shape parameters and their spatio-temporal evolution. The procedures are demonstrated using Cine CT images of the human LV in normal and pathological cases.

Evaluation of cardiac function with magnetic resonance imaging

A large body of evidence has accumulated to substantiate the accuracy of functional MR measurements of both ventricles. Because of good accuracy and superior reproducibility, MR imaging may be considered the gold standard for in vivo quantification of left and right ventricular ejection fraction, myocardial mass, and wall stress. New prospects for functional MR imaging include determination of the end-systolic volume-pressure relation as an index of myocardial contractility. The ability of MR imaging to detect wall motion disturbances may be enhanced further by combining myocardial tagging techniques with finite element analysis. Conventional MR imaging is limited by long examination times, but recent ultrafast modifications of echo-planar imaging allow completion of a functional heart study within seconds. Implementation of ultrafast MR imaging will greatly increase the usefulness of MR imaging for routine evaluation of cardiac function.

Assessment and visualization of the curvature of the left ventricle from 3D medical images

We address the problem of using curvature features to assess the three-dimensional (3D) motion of the left ventricle. The adequacy of this approach depends on the actual characteristics of the curvature of the left ventricle and particularly on the spatial and temporal stability of these features. From experimental data, we compute the distribution of the Gaussian curvature over the surface of the left ventricle by using an iterative procedure. The results are visualized in 3D through a voxel-based surface rendering technique. We show that the Gaussian curvature remains stable along the cardiac cycle. This curvature feature could thus provide a reliable basis for further 3D motion analysis of the left ventricle.