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

3D myocardial tissue tracking with slice followed cine DENSE MRI

PURPOSE

To track three-dimensional (3D) myocardial tissue motion using slice followed cine displacement encoded imaging with stimulated echoes (DENSE).

MATERIALS AND METHODS

Slice following (SF) has previously been developed for 2D myocardial tagging to compensate for the effect of through-plane motion on 2D tissue tracking. By incorporating SF into a cine DENSE sequence, and applying displacement encoding in three orthogonal directions, we demonstrate the ability to track discrete elements of a slice of myocardium in 3D as the heart moves through the cardiac cycle. The SF cine DENSE tracking algorithm was validated on a moving phantom, and the effects of through-plane motion on 2D cardiac strain were investigated in six healthy subjects.

RESULTS

A through-plane tracking accuracy of 0.46 +/- 0.32 mm was measured for a typical range of myocardial motion using a rotating phantom. In vivo 3D measurements of cardiac motion were consistent with prior myocardial tagging results. Through-plane rotation in a mid-ventricularshort-axis view was shown to decrease the magnitude of the 2D end-systolic circumferential strain by 3.91 +/- 0.43% and increase the corresponding radial strain by 6.01 +/- 1.07%.

CONCLUSION

Slice followed cine DENSE provides an accurate method for 3D tissue tracking.

A model-based time-reversal of left ventricular motion improves cardiac motion analysis using tagged MRI data

BACKGROUND

Myocardial motion is an important observable for the assessment of heart condition. Accurate estimates of ventricular (LV) wall motion are required for quantifying myocardial deformation and assessing local tissue function and viability. Harmonic Phase (HARP) analysis was developed for measuring regional LV motion using tagged magnetic resonance imaging (tMRI) data. With current computer-aided postprocessing tools including HARP analysis, large motions experienced by myocardial tissue are, however, often intractable to measure. This paper addresses this issue and provides a solution to make such measurements possible.

METHODS

To improve the estimation performance of large cardiac motions while analyzing tMRI data sets, we propose a two-step solution. The first step involves constructing a model to describe average systolic motion of the LV wall within a subject group. The second step involves time-reversal of the model applied as a spatial coordinate transformation to digitally relax the contracted LV wall in the experimental data of a single subject to the beginning of systole. Cardiac tMRI scans were performed on four healthy rats and used for developing the forward LV model. Algorithms were implemented for preprocessing the tMRI data, optimizing the model parameters and performing the HARP analysis. Slices from the midventricular level were then analyzed for all systolic phases.

RESULTS

The time-reversal operation derived from the LV model accounted for the bulk portion of the myocardial motion, which was the average motion experienced within the overall subject population. In analyzing the individual tMRI data sets, removing this average with the time-reversal operation left small magnitude residual motion unique to the case. This remaining residual portion of the motion was estimated robustly using the HARP analysis.

CONCLUSION

Utilizing a combination of the forward LV model and its time reversal improves the performance of motion estimation in evaluating the cardiac function.

Quantification of regional left ventricular dyssynchrony by magnetic resonance imaging

Cardiac resynchronization therapy is an established treatment in patients with symptomatic heart failure and intraventricular conduction delay. Electrical dyssynchrony is typically adopted to represent myocardial activation dyssynchrony, which should be compensated by cardiac resynchronization therapy. One third of the patients, however, does not respond to the therapy. Therefore, imaging modalities aimed at the mechanical dyssynchrony estimation have been recently proposed to improve patient selection criteria. This paper presents a novel fully automated method for regional mechanical left ventricular dyssynchrony quantification in short-axis magnetic resonance imaging. The endocardial movement is described by time-displacement curves with respect to an automatically determined reference point. Different methods are proposed for time-displacement curve analysis aimed at the regional contraction timing estimation. These methods were evaluated in two groups of subjects with (nine patients) and without (six patients) left bundle branch block. The contraction timing standard deviation showed a significant increase for left bundle branch block patients with all the methods. A novel method based on phase spectrum analysis may be however preferred due to a better specificity (99.7%) and sensitivity (99.0%). In conclusion, this method provides a valuable prognostic indicator for heart failure patients with dyssynchronous ventricular contraction and it opens new possibilities for regional timing analysis.

Generation of a cardiac shape model from CT data

In this paper we describe the generation of a geometric cardiac shape model based on cardiac CTA data. The model includes the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model for the end-diastolic heart has been built based on 27 end-diastolic cardiac CTA datasets and a mean motion model based on 11 multiphase datasets. The model has been evaluated with respect to its capability to estimate the position of cardiac structures. Allowing a similarity transformation to adapt the model to image data, cardiac surface positions can be predicted with an accuracy of below 5 mm.

Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model

Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.

Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI

We present a framework for the analysis of short axis cardiac MRI, using statistical models of shape and appearance. The framework integrates temporal and structural constraints and avoids common optimization problems inherent in such high dimensional models. The first contribution is the introduction of an algorithm for fitting 3D active appearance models (AAMs) on short axis cardiac MRI. We observe a 44-fold increase in fitting speed and a segmentation accuracy that is on par with Gauss-Newton optimization, one of the most widely used optimization algorithms for such problems. The second contribution involves an investigation on hierarchical 2D+time active shape models (ASMs), that integrate temporal constraints and simultaneously improve the 3D AAM based segmentation. We obtain encouraging results (endocardial/epicardial error 1.43+/-0.49 mm/1.51+/-0.48 mm) on 7980 short axis cardiac MR images acquired from 33 subjects. We have placed our dataset online, for the community to use and build upon.

A new variational technique for combining affine registration and optical flow in echocardiography images

Since myocardial motion is directly related to cardiac vascular supply, it can be helpful in diagnosing the heart abnormalities. The most comprehensive and available imaging study of the cardiac function is B-Mode echocardiography. However diagnostic systems are expert dependent and motion is not clear in the B-mode echocardiography images and therefore many efforts are pointed toward proposing new methods to measure the motion accurately. So far there have been many methods for myocardial motion estimation such as affine registration, elastic registration or optical flow but each method suffers from lack of accuracy. To increase the accuracy of motion detection techniques, we propose a new algorithm based on a variational technique to combine the efficiencies of optical flow methods and affine registration in combination with multi-resolution spatiotemporal Spline moments. The evaluation was performed on simulated, synthetic and real data. A comparison between the proposed method and several other methods shows its better performance to measure the motion more accurately in the presence of shear, rotation and noise. The proposed method achieved rotational error of 2.6 degrees per frame and amplitude error of 3.7 percent per frame. These results demonstrate a better efficiency with respect to other B-Mode echocardiography motion estimation techniques such as Lucas-Kanade, Horn-Schunck and spatiotemporal affine technique.

Isolation of the left atrial surface from cardiac multi-detector CT images based on marker controlled watershed segmentation

The delineation of left atrium (LA) and pulmonary veins (PVs) anatomy from high resolution images holds importance for atrial fibrillation (AF) investigation and treatment. In this study, a semiautomatic segmentation procedure for LA and PVs inner surface from contrast enhanced CT data was developed. The procedure consists of a three dimensional marker controlled watershed segmentation applied to the external morphological gradient, followed by variable threshold surface extraction from the original intensity image. A preliminary anisotropic non-linear filtering was implemented to improve the S/N ratio of CT images. The performance of segmentation was evaluated on cardiac CT scans of 12 AF patients both qualitatively and quantitatively. The qualitative evaluation by expert radiologist assessed the segmentation as overall successful in all patients and capable of extracting both the LA body and the connected vascular trees. The quantitative validation, by computing discrepancy measures with respect to a manually segmented gold standard, indicated an average of about 90% of voxels correctly classified and an average border mismatch lower than 1.5 voxels (1.2 mm). The accurate extraction of the inner LA-PVs walls provided by this method, along with the minimal required human intervention, should facilitate the use of anatomical atrial models for the non-pharmacological treatment of AF.

Segmentation of Left Ventricle From 3D Cardiac MR Image Sequences Using A Subject-Specific Dynamical Model

Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of cardiac shapes and deformations. In this paper, we use a subject-specific dynamical model (SSDM) that handles inter-subject variability and temporal dynamics (intra-subject variability) simultaneously. It can progressively identify the specific motion patterns of a new cardiac sequence based on the segmentations observed in the past frames. We formulate the integration of the SSDM into the segmentation process in a recursive Bayesian framework in order to segment each frame based on the intensity information from the current frame and the prediction from the past frames. We perform "Leave-one-out" test on 32 sequences to validate our approach. Quantitative analysis of experimental results shows that the segmentation with the SSDM outperforms those with the SM and GDM by having better global and local consistencies with the manual segmentation.

Sampled-data filtering framework for cardiac motion recovery: optimal estimation of continuous dynamics from discrete measurements

Quantitative and noninvasive estimation of cardiac kinematics has significant physiological and clinical implications. In this paper, a sampled-data filtering framework is presented for the recovery of cardiac motion and deformation functions from periodic medical image sequences. Cardiac dynamics is a continuously evolving physical/physiological process, whereas the imaging data can provide only sampled measurements at discrete time instants. Given such a hybrid paradigm, stochastic multiframe filtering frameworks are constructed to couple the continuous dynamics with the discrete measurements, and to coordinately deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data. The state estimates are predicted according to the continuous-time biomechanically constructed state equation between observation time points, and then updated with the new imaging-derived measurements at discrete time instants, yielding physically more meaningful and more accurate estimation results. Both continuous-discrete Kalman filter and sampled-data Hinfinity filter are applied for motion recovery. While Kalman filter is the optimal estimator under Gaussian noises, the Hinfinity scheme can give robust estimation results when the types and levels of model uncertainties and data disturbances are not available a priori. The strategies are validated through synthetic data experiments to illustrate their advantages and on canine MR phase contrast images and human MR tagging data to show their clinical potential.

Physiome-model-based state-space framework for cardiac deformation recovery

RATIONALE AND OBJECTIVES

To more reliably recover cardiac information from noise-corrupted, patient-specific measurements, it is essential to employ meaningful constraining models and adopt appropriate optimization criteria to couple the models with the measurements. Although biomechanical models have been extensively used for myocardial motion recovery with encouraging results, the passive nature of such constraints limits their ability to fully count for the deformation caused by active forces of the myocytes. To overcome such limitations, we propose to adopt a cardiac physiome model as the prior constraint for cardiac motion analysis.

MATERIALS AND METHODS

The cardiac physiome model comprises an electric wave propagation model, an electromechanical coupling model, and a biomechanical model, which are connected through a cardiac system dynamics for a more complete description of the macroscopic cardiac physiology. Embedded within a multiframe state-space framework, the uncertainties of the model and the patient's measurements are systematically dealt with to arrive at optimal cardiac kinematic estimates and possibly beyond.

RESULTS

Experiments have been conducted to compare our proposed cardiac-physiome-model-based framework with the solely biomechanical model-based framework. The results show that our proposed framework recovers more accurate cardiac deformation from synthetic data and obtains more sensible estimates from real magnetic resonance image sequences.

CONCLUSION

With the active components introduced by the cardiac physiome model, cardiac deformations recovered from patient's medical images are more physiologically plausible.

A device guidance method for organ motion compensation in MRI-guided therapy

Organ motion compensation in image-guided therapy is an active area of research. However, there has been little research on motion tracking and compensation in magnetic resonance imaging (MRI)-guided therapy. In this paper, we present a method to track a moving organ in MRI and control an active mechanical device for motion compensation. The method proposed is based on MRI navigator echo tracking enhanced by Kalman filtering for noise robustness. We also developed an extrapolation scheme to resolve any discrepancies between tracking and device control sampling rates. The algorithm was tested in a simulation study using a phantom and an active mechanical tool holder. We found that the method is feasible to use in a clinical MRI scanner with sufficient accuracy (0.36 mm to 1.51 mm depending on the range of phantom motion) and is robust to noise. The method proposed may be useful in MRI-guided targeted therapy, such as focused ultrasound therapy for a moving organ.

Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging

Statistical partitioning of images into meaningful areas is the goal of all region-based segmentation algorithms. The clustering or creation of these meaningful partitions can be achieved in number of ways but in most cases it is achieved through the minimization or maximization of some function of the image intensity properties. Commonly these optimization schemes are locally convergent, therefore initialization of the parameters of the function plays a very important role in the final solution. In this paper we perform an automatically initialized expectation-maximization algorithm to partition the data in medical MRI images. We present analysis and illustrate results against manual initialization and apply the algorithm to some common medical image processing tasks.

Breast image registration techniques: a survey

Breast cancer is the most common type of cancer in women worldwide. Image registration plays an important role in breast cancer detection. This paper gives an overview of the current state-of-the-art in the breast image registration techniques. For the intramodality registration techniques, X-ray, MRI, and ultrasound are the primary focuses of interest. Intermodality techniques will cover the combination of different modalities. Validation of breast registration methods is also discussed.

4D shape priors for a level set segmentation of the left myocardium in SPECT sequences

We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.

State-space analysis of cardiac motion with biomechanical constraints

Quantitative estimation of nonrigid motion from image sequences has important technical and practical significance. State-space analysis provides powerful and convenient ways to construct and incorporate the physically meaningful system dynamics of an object, the image-derived observations, and the process and measurement noise disturbances. In this paper, we present a biomechanical-model constrained state-space analysis framework for the multiframe estimation of the periodic cardiac motion and deformation. The physical constraints take the roles as spatial regulator of the myocardial behavior and spatial filter/interpolator of the data measurements, while techniques from statistical filtering theory impose spatiotemporal constraints to facilitate the incorporation of multiframe information to generate optimal estimates of the heart kinematics. Physiologically meaningful results have been achieved from estimated displacement fields and strain maps using in vivo left ventricular magnetic resonance tagging and phase contrast image sequences, which provide the tag-tag and tag-boundary displacement inputs, and the mid-wall instantaneous velocity information and boundary displacement measures, respectively.

A simultaneous framework for recovering three dimensional shape and nonrigid motion from cardiac image sequences

Quantitative assessment of the shape and motion variability of the heart has important implications for the diagnosis and treatment of cardiac diseases. In this paper, we present a unified methodology which simultaneously recovers the shape and motion of the left ventricle, including the endo-, epi-, and mid-wall myocardium. The left ventricle is modeled as an isotropic linear elastic material, and represented by volumetric mesh constructed from the Delaunay triangulation of the sampling points. Specifically, the evolution forces imposed on the myocardium are individually constructed for each nodal point through the integration of the data-driven edginess measures, the prior spatial distributions of the myocardial tissues, the temporal coherence of the image-derived salient features, and the cyclic motion characteristics of the heart. The dense displacement field can then be estimated when the total energy of the elastic body is minimized at equilibrium. Experiments on 3D human magnetic resonance image sequences of heathy and pathological subjects show the accuracy and robustness of the strategy.

Cardiac motion analysis using nonlinear biomechanical constraints

Quantitative assessment of the motion and deformation variability of the heart has important implications for the understanding, diagnosis, and treatment of cardiac diseases. Various existing image-based approaches typically rely on linear constraining models which are however physically implausible. In this paper, we present a biomechnically constrained framework for the estimation of left ventricle deformation from medical image sequences using more realistic nonlinear geometry and material models. Once the myocardial boundaries and the sparse corresponding boundary points are derived from an active region model, we construct the cardiac dynamics where the left ventricle is modelled as an Mooney-Rivlin material undergoing large deformation. We then rely on the Newmark scheme to perform frame-to-frame estimation of the cardiac motion/deformation parameters. Experiments have been performed with 3D cardiac MR image sequences with very encouraging results.

Vector Interpolation Method in Three-dimensional Reconstruction of Tissue Doppler Echocardiography

A new method of vector interpolation in three-dimensional reconstruction of tissue Doppler echocardiography is developed and applied to the clinical research. Both the amplitude and direction information of functional parameters, such as velocity or acceleration, in tissue Doppler images is utilized to reconstruct dynamic three-dimensional vector field of myocardial tissue motion. The reconstructed three-dimensional field tissue motion can be visualized simultaneously with the three-dimensional anatomical structure to express the correlation between them. Clinical human experiments show that reconstructed results are consistent with the physiological characteristics of the heart. This method may have potential application in the study of cardiac electrophysiology.

3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach

We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed.

Shape-based myocardial contractility analysis using multivariate outlier detection

This paper presents a new approach to regional myocardial contractility analysis based on inter-landmark motion (ILM) vectors and multivariate outlier detection. The proposed spatio-temporal representation is used to describe the coupled changes occurring at pairs of regions of the left ventricle, thus enabling the detection of geometrical and dynamic inconsistencies. Multivariate tolerance regions are derived from training samples to describe the variability within the normal population using the ILM vectors. For new left ventricular datasets, outlier detection enables the localization of extreme ILM observations and the corresponding myocardial abnormalities. The framework is validated on a relatively large sample of 50 subjects and the results show promise in localization and visualization of regional left ventricular dysfunctions.

Novel multistage three-dimensional medical image segmentation: methodology and validation

In this paper, we propose a novel multistage method for three-dimensional (3-D) segmentation of medical images and a new radial distance-based segmentation validation approach. For the 3-D segmentation method, we first employ a morphological recursive erosion operation to reduce the connectivity between the region of interest and its surrounding neighborhood; then we design a hybrid segmentation method to achieve an initial result. The hybrid approach integrates an improved fast marching method and a morphological reconstruction algorithm. Finally, a morphological recursive dilation is employed to recover any lost structure from the first stage of the multistage method. This approach is tested on 12 CT and 3 MRI images of the brain, heart, and kidney, to demonstrate the effectiveness and accuracy of this technique across a variety of imaging modalities and organ systems. In order to validate the multistage segmentation method, a novel radial distance-based validation method is proposed that uses a global accuracy (GA) measure. The GA is calculated based on local radial distance errors (LRDE), where LRDE are calculated on the radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. The experimental results demonstrate that the proposed multistage segmentation method is fast and accurate, with comparable performance to existing segmentation methods, but with a significantly higher execution speed.

Automatic contour propagation in cine cardiac magnetic resonance images

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction.

A comprehensive shape model of the heart

Domain knowledge about the geometrical properties of cardiac structures is an important ingredient for the segmentation of these structures in medical images or for the simulation of cardiac physiology. So far, a strong focus was put on the left ventricle due to its importance for the general pumping performance of the heart and related functional indices. However, other cardiac structures are of similar importance, e.g., the coronary arteries with respect to diagnosis and treatment of arteriosclerosis or the left atrium with respect to the treatment of atrial fibrillation. In this paper we describe the generation of a geometric cardiac model including the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model for the end-diastolic heart has been built based on 27 cardiac CT datasets and has been evaluated with respect to its capability to estimate the position of cardiac structures. Allowing a similarity transformation to adapt the model to image data, cardiac surface positions can be predicted with an accuracy of below 5mm.

Towards pointwise motion tracking in echocardiographic image sequences – Comparing the reliability of different features for speckle tracking

In this paper, we studied the problem of feature-based motion tracking in echocardiographic image sequences. We described the relation between possible feature variations and different kinds of tissue motion using a linear convolution model. We also showed that motion-feature decorrelation (which means that the motion parameters estimated using feature tracking fail to represent the underlying tissue motion) compensation is an ill-posed inverse problem. Instead of finding a method that may provide better compensation results than previous approaches, we used an quantitative measure to compare the reliability of tracking features. Experiment results showed that the use of the reliability measure improved the robustness of displacement estimation. With the help of the reliability measure, we compared the performance of different features using simulations and phantom examples. While we noticed that the radio frequency (RF) signal outperforms the B-mode (BM) signal in the analysis of small deformation (e.g., less than 0.1% compression), we also found out that the BM signal works better than the RF signal in the analysis of large deformation (e.g., larger than 2% compression). The use of a band-passed filtered feature does not result in significant improvement in tracking.

Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

Development of quantification software using model-based segmentation of left ventricular myocardium in gated myocardial SPECT

Gated myocardial single photon emission computed tomography (SPECT) is being used for the diagnosis of coronary artery diseases. In this study, we developed new software for the quantification of volumes and ejection fraction (EF) on the gated myocardial SPECT data using a cylindrical model. Volumes and EF by developed software were validated by comparing with those quantified by quantitative gated SPECT (QGS) software. Cylinder model for left ventricular myocardium was used to eliminate background activity and count profiles across the myocardium were fitted to the Gaussian curve to determine the endocardial and epicardial boundary. End-diastolic volume (EDV), end-systolic volume (ESV) and EF were calculated using this boundary information. Gated myocardial SPECT was performed in 83 patients. EDV, ESV and EF values estimated using present method were compared to those obtained using the commercialized software QGS, and reproducibility in the parameter estimation was assessed. EF, EDV and ESV obtained using two methods were correlated well (correlation coefficients = 0.96, 0.96 and 0.98). The correlation between the parameters repetitively estimated from the same data set by an operator was very high (correlation coefficients = 0.96, 0.99 and 0.99 for EF, EDV and ESV). On the repeated acquisition, reproducibility was also high with correlation coefficients of 0.89, 0.97 and 0.98. The present software will be useful for the development of new parameters for describing the perfusion and function of the LV.

Weaning From Mechanical Ventilation: A Retrospective Analysis Leading to a Multimodal Perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.

Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties

In this paper, we present a framework to estimate local ventricular myocardium contractility using clinical MRI, a heart model and data assimilation. First, we build a generic anatomical model of the ventricles including muscle fibre orientations and anatomical subdivisions. Then, this model is deformed to fit a clinical MRI, using a semi-automatic fuzzy segmentation, an affine registration method and a local deformable biomechanical model. An electromechanical model of the heart is then presented and simulated. Finally, a data assimilation procedure is described, and applied to this model. Data assimilation makes it possible to estimate local contractility from given displacements. Presented results on fitting to patient-specific anatomy and assimilation with simulated data are very promising. Current work on model calibration and estimation of patient parameters opens up possibilities to apply this framework in a clinical environment.

An electromechanical model of the heart for image analysis and simulation

This paper presents a new three-dimensional electromechanical model of the two cardiac ventricles designed both for the simulation of their electrical and mechanical activity, and for the segmentation of time series of medical images. First, we present the volumetric biomechanical models built. Then the transmembrane potential propagation is simulated, based on FitzHugh-Nagumo reaction-diffusion equations. The myocardium contraction is modeled through a constitutive law including an electromechanical coupling. Simulation of a cardiac cycle, with boundary conditions representing blood pressure and volume constraints, leads to the correct estimation of global and local parameters of the cardiac function. This model enables the introduction of pathologies and the simulation of electrophysiology interventions. Moreover, it can be used for cardiac image analysis. A new proactive deformable model of the heart is introduced to segment the two ventricles in time series of cardiac images. Preliminary results indicate that this proactive model, which integrates a priori knowledge on the cardiac anatomy and on its dynamical behavior, can improve the accuracy and robustness of the extraction of functional parameters from cardiac images even in the presence of noisy or sparse data. Such a model also allows the simulation of cardiovascular pathologies in order to test therapy strategies and to plan interventions.

Automatic segmentation of the left ventricle cavity and myocardium in MRI data

A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method.

Regional analysis of the left ventricle of the heart

This paper presents a method to analyze the local wall motion of the left ventricle of the heart. Data are sets of points (obtained from various medical imaging modalities) corresponding to surfaces of the left ventricle, which evolve as a function of time. After re-sampling, the surfaces are segmented in order to create regions of equivalent volume. Then, the local cardiac parameters are estimated: evolution of the regional volumes as a function of time, ejection fraction, end-diastolic and end-systolic volumes, end-diastolic and end-systolic instants. The method has been validated using deformable surfaces synthesized from an ellipsoidal model. It has also been tested in vivo on a set of 59 patients using a specially developed software product, which satisfies severe constraints of robustness, real-time, interactivity and ergonomics. The results obtained are similar to those provided by a reference nuclear medicine examination, but the proposed method is faster and gives a more precise localization of the cardiac wall motion anomalies.

Assessment of Left Ventricular Function in Cardiac MSCT Imaging by a 4D Hierarchical Surface-Volume Matching Process

Multislice computed tomography (MSCT) scanners offer new perspectives for cardiac kinetics evaluation with 4D dynamic sequences of high contrast and spatiotemporal resolutions. A new method is proposed for cardiac motion extraction in multislice CT. Based on a 4D hierarchical surface-volume matching process, it provides the detection of the heart left cavities along the acquired sequence and the estimation of their 3D surface velocity fields. A Markov random field model is defined to find, according to topological descriptors, the best correspondences between a 3D mesh describing the left endocardium at one time and the 3D acquired volume at the following time. The global optimization of the correspondences is realized with a multiresolution process. Results obtained on simulated and real data show the capabilities to extract clinically relevant global and local motion parameters and highlight new perspectives in cardiac computed tomography imaging.

Segmentation of 4D cardiac images: investigation on statistical shape models

The purpose of this research was two-fold: (1) to investigate the properties of statistical shape models constructed from manually segmented cardiac ventricular chambers to confirm the validity of an automatic 4-dimensional (4D) segmentation model that uses gradient vector flow (GVF) images of the original data and (2) to develop software to further automate the steps necessary in active shape model (ASM) training. These goals were achieved by first constructing ASMs from manually segmented ventricular models by allowing the user to cite entire datasets for processing using a GVF-based landmarking procedure and principal component analysis (PCA) to construct the statistical shape model. The statistical shape model of one dataset was used to regulate the segmentation of another dataset according to its GVF, and these results were then analyzed and found to accurately represent the original cardiac data when compared to the manual segmentation results as the golden standard.

Biventricular myocardial strains via nonrigid registration of anatomical NURBS model [corrected]

We present research in which both left and right ventricular deformation is estimated from tagged cardiac magnetic resonance imaging using volumetric deformable models constructed from nonuniform rational B-splines (NURBS). The four model types considered and compared for the left ventricle include two Cartesian NURBS models--one with a cylindrical parameter assignment and one with a prolate spheroidal parameter assignment. The remaining two are non-Cartesian, i.e., prolate spheroidal and cylindrical each with their respective prolate spheroidal and cylindrical parameter assignment regimes. These choices were made based on the typical shape of the left ventricle. For each frame starting with end-diastole, a NURBS model is constructed by fitting two surfaces with the same parameterization to the corresponding set of epicardial and endocardial contours from which a volumetric model is created. Using normal displacements of the three sets of orthogonal tag planes as well as displacements of contour/tag line intersection points and tag plane intersection points, one can solve for the optimal homogeneous coordinates, in a weighted least squares sense, of the control points of the deformed NURBS model at end-diastole using quadratic programming. This allows for subsequent nonrigid registration of the biventricular model at end-diastole to all later time frames. After registration of the model to all later time points, the registered NURBS models are temporally lofted in order to create a comprehensive four-dimensional NURBS model. From the lofted model, we can extract three-dimensional myocardial deformation fields and corresponding Lagrangian and Eulerian strain maps which are local measures of nonrigid deformation. The results show that, in the case of simulated data, the quadratic Cartesian NURBS models with the cylindrical and prolate spheroidal parameter assignments outperform their counterparts in predicting normal strain. The decreased complexity associated with the Cartesian model with the cylindrical parameter assignment prompted its use for subsequent calculations. Lagrangian strains in three canine data, a normal human, and a patient with history of myocardial infarction are presented. Eulerian strains for the normal human data are also included.

Time Continuous Tracking and Segmentation of Cardiovascular Magnetic Resonance Images Using Multidimensional Dynamic Programming

In this article, we propose a semiautomatic method for time-continuous contour detection in all phases of the cardiac cycle in magnetic resonance sequences. The method is based on multidimensional dynamic programming. After shape parameterization, cost hypercubes are filled with image-feature derived cost function values. Using multidimensional dynamic programming, an optimal path is sought through the sequence of hypercubes. Constraints can be imposed by setting limits to the parameter changes between subsequent hypercubes. Quantitative evaluation was performed on 20 subjects. Average border positioning error over all slices, all phases and all studies, was 1.77 +/- 0.57 mm for epicardial and 1.86 +/- 0.59 mm for endocardial contours. The average error in end-diastolic and end-systolic volumes over all studies was small: 4.24 +/- 4.62 mL and -4.36 +/- 4.26 mL, respectively. The average error in ejection fraction was 4.82 +/- 3.01%. The reported results compare favorable to the best-reported results in recent literature, underlining the potential of this method for application in daily clinical practice.

Endocardium and epicardium contour modeling based on Markov Random Fields and active contours

A segmentation application prototype of the volume of the left ventricle for Magnetic Resonance Imaging is being developed. The foundation for this work is given by modeling possible radial deformations of the epicardium and endocardium contours by means of a Markov Random Field over which the most probable configuration is estimated. The field makes use of a Bayesian approach based on a priori terms which impose smoothness along the coupled contours and likelihood terms which gather information provided by the images about the areas where the contours are supposed to be. The parameters of the field are estimated on a supervised basis.

A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures

We address the problem of quantitatively assessing myocardial function from tagged MRI sequences. We develop a two-step method comprising (i) a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and (ii) a measurement step, yielding local and segmental deformation parameters over the whole myocardium. Experiments on healthy and pathological data demonstrate that this method delivers, within a reasonable computation time and in a fully unsupervised way, reliable measurements for normal subjects and quantitative pathology-specific information. Beyond cardiac MRI, this work redefines the foundations of variational non-rigid registration for information-theoretic similarity criteria with potential interest in multimodal medical imaging.

Correction of motion artifacts from cardiac cine magnetic resonance images

RATIONALE AND OBJECTIVES

An image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images.

MATERIALS AND METHODS

The location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods.

RESULTS

The algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10(-9)).

CONCLUSIONS

The novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.

Fast tracking of cardiac motion using 3D-HARP

Magnetic resonance (MR) tagging is capable of accurate, noninvasive quantification of regional myocardial function. Routine clinical use, however, is hindered by cumbersome and time-consuming postprocessing procedures. We propose a fast, semiautomatic method for tracking three-dimensional (3-D) cardiac motion from a temporal sequence of short- and long-axis tagged MR images. The new method, called 3-D-HARmonic Phase (3D-HARP), extends the HARP approach, previously described for two-dimensional (2-D) tag analysis, to 3-D. A 3-D material mesh model is built to represent a collection of material points inside the left ventricle (LV) wall at a reference time. Harmonic phase, a material property that is time-invariant, is used to track the motion of the mesh through a cardiac cycle. Various motion-related functional properties of the myocardium, such as circumferential strain and left ventricular twist, are computed from the tracked mesh. The correlation analysis of 3D-HARP and FINDTAGS + Tag Strain(E) Analysis (TEA), which are well-established tag analysis techniques, shows that the regression coefficients of circumferential strain (E(CC)) and twist angle are r2 = 0.8605 and r2 = 0.8645, respectively. The total time required for tracking 3-D cardiac motion is approximately 10 min in a 9 timeframe tagged MRI dataset and has the potential to be much faster.

Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models

Real-time three-dimensional (3-D) echocardiography is a new imaging modality that presents the unique opportunity to visualize the complex 3-D shape and motion of the left ventricle (LV) in vivo and to measure the associated global and local function parameters. To take advantage of this opportunity in routine clinical practice, automatic segmentation of the LV in the 3-D echocardiographic data, usually hundreds of megabytes large, is essential. We report a new segmentation algorithm for this task. Our algorithm has two distinct stages, initialization of a deformable model and its refinement, which are connected by a dual "voxel + wiremesh" template. In the first stage, mutual-information-based registration of the voxel template with the image to be segmented helps initialize the wiremesh template. In the second stage, the wiremesh is refined iteratively under the influence of external and internal forces. The internal forces have been customized to preserve the nonsymmetric shape of the wiremesh template in the absence of external forces, defined using the gradient vector flow approach. The algorithm was validated against expert-defined segmentation and demonstrated acceptable accuracy. Our segmentation algorithm is fully automatic and has the potential to be used clinically together with real-time 3-D echocardiography for improved cardiovascular disease diagnosis.

Consistent Estimation of Cardiac Motions by 4D Image Registration

A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally smooth, thereby overcoming potential limitations of some methods that typically estimate cardiac deformation sequentially from one frame to another, instead of treating the entire set of images as a 4D volume. To facilitate our image matching process, an attribute vector is designed for each point in the image to include intensity, boundary and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points for refinement of registration. Experimental results on real data demonstrate good performance of the proposed method in registering cardiac images and estimating motions from cardiac image sequences.

Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification

In this paper we present a novel method for building a 4D statistical atlas describing the cardiac anatomy and how the cardiac anatomy changes during the cardiac cycle. The method divides the distribution space of cardiac shapes into two subspaces. One distribution subspace accounts for changes in cardiac shape caused by inter-subject variability. The second distribution subspace accounts for changes in cardiac shape caused by deformation during the cardiac cycle (i.e. intra-subject variability). Principal component analysis (PCA) have been performed in order to calculate the most significant modes of variation of each distribution subspace. During the construction of the statistical atlas we eliminate the need for manual landmarking of the cardiac images by using a non-rigid surface registration algorithm to propagate a set of pseudo-landmarks from an automatically landmarked atlas to each frame of all the image sequences. In order to build the atlas we have used 26 cardiac image sequences from healthy volunteers. We show how the resulting statistical atlas can be used to differentiate between cardiac image sequences from patients with hypertrophic cardiomyopathy and normal subjects.

Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration

Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction using image registration and voxel based similarity measures. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the normalized mutual information between the images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.

Fast tracking of cardiac motion using 3D-HARP

MR tagging is considered as a valuable technique to evaluate regional myocardial function quantitatively and noninvasively, however the cumbersome and time-consuming post-processing procedures for cardiac motion tracking still hinder its application to routine clinical examination. We present a fast and semiautomatic method for tracking 3D cardiac motion from short-axis (SA) and long-axis (LA) tagged MRI images. The technique, called 3D-HARP (HARmonic Phase), is based on the HARP method and extends this method to track 3D motion. A material mesh model is built to represent a collection of material points inside the left ventricle (LV) wall. The phase time-invariance property of material points is used to track the mesh points. For a series of 9 timeframe MRI images, the total time required for initializing settings, building the mesh, and tracking 3D cardiac motion is approximately 10 minutes. Further analysis of Langrangian strain and twist angle demonstrates that during systole, the lateral LV wall shows a greater strain values than the septum and the SA slices from the base to the apex show a gradual change in twist pattern.

An approach to the three-dimensional display of left ventricular function and viability using MRI

Cardiac MRI was performed in human volunteers to determine the magnitude of the misregistration (MSR) of cardiac landmarks due to variability in the diaphragm position for repeated breath-holds. Seven normal volunteers underwent MR imaging of the left ventricle (LV) to evaluate the magnitude of the endocardial centroid MSR. The MSR for a mid-ventricle short-axis image was 3.01 +/- 1.68 mm through-plane and 4.16 +/- 1.62 mm in-plane. A second order polynomial fit through the LV centroid coordinates minimized the in-plane component of the MSR error. Short-axis cine images, corrected for MSR, provided high-resolution 2D data from which an accurate anatomical model of the LV was generated. Anatomical landmarks were used to register parametric maps of myocardial perfusion and viability to the three-dimensional (3D) model, with the corresponding parameters displayed as color-encoded values on the endo- and epicardial surfaces of the LV. Registration of regional wall motion, perfusion and viability to the 3D model was performed for three patients with a history of cardiovascular disease. The proposed 3D reconstruction technique allows visualization in 3D of the LV anatomy, in combination with parametric mapping of its functional status.