Functional Imaging and Modeling of the Heart

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Patient-specific computational modeling of the atria can provide a better understanding about mechanisms underlying the arrhythmia and will potentially be used for model-based ablation therapy evaluation and planning. Electrical excitation spreads from the left to the right atrium at discrete locations. The location of the muscular bridges cannot be determined from image data. In the present study, left atrial activation sources were manually identified in local activation time maps of 4 AF patients. This information was used to adjust rule-based placed interatrial bridges in anatomical atrial models of the patients. Sinus rhythm simulations showed a better qualitative agreement to the measured left atrial activation patterns after the adjustment of the bridges. For one patient, the simulated body surface potential (BSP) pattern after the adjustment correlated better to measured BSP maps. The results show that the fusion of intracardiac electrical measurements of early left atrial activation can be used to refine patient atria models with information of the myocardial structure which cannot be imaged. In future, such personalized atrial models may be used to support EP interventions.

Understanding the myocardial biomechanics of the left ventricle (LV) in health and disease is important for improving patient risk stratification and management. Computational models of the heart are able to provide insights into the mechanics of heart function. In this study, we develop a dynamic human LV model using an immersed boundary (IB) method along with a finite element description of myocardial mechanics. Our results show that this computational model is able to simulate LV dynamics from end-diastole to end-systole, and that the model results are in reasonably good agreement with noninvasive in vivo strain measurements obtained by magnetic resonance (MR) imaging.

Echocardiographic transducers record two-dimensional (2D) datasets in a sector reference after which a scan-conversion is applied to obtain the images in Cartesian coordinates. To assess left ventricular(LV) flow dynamics by a low dose contrast injection, we recently developed a 2D tracking methodology by combining speckle tracking (ST) with Navier-Stokes based regularization and it has been tested in synthetic ultrasound datasets prior to the scan conversion. However, in clinical settings the estimation becomes challenging due to the inhomogeneous image patterns which are inherently introduced by scan-conversion and are more likely to be locally strengthened by non homogeneous bubble seeding and high velocity gradient. To better deal with that, the aim of this study was hereby to modify the previous method by using a dynamic tracking kernel size. Its performance was first quantified in synthetic scan-converted ultrasound data based on a computational fluid dynamics model of LV flow. The applicability of the approach was tested in an experimental phantom setup with pulsed flow that mimics the normal human heart and simultaneously allows for optical particle image velocimetry as a standard reference technique. Both qualitative and quantitative comparison of the estimated flow fields and reference measurements showed that the modified methodology can correctly characterize the flow field properties and is promising to offer new insights into the flow dynamics inside the left ventricle.

A bilayer surface model of human atria is presented. A rule-based bi-layer physiological fibre arrangement is defined on an imaged in-vivo atrial geometry. This fibre architecture includes the main structures of fibres in the right and left atria, including the main transmural heterogeneities and transseptal connexions.

The precision of the corresponding bilayer mathematical model is assessed by comparing the case of two sheets of orthogonal fibres to a three dimensional slab of tissue. A comparison of bilayer and monolayer simulations of sinus propagation is finally proposed.

This model allows inclusion of transmural heterogeneities while maintaining small computational costs associated with surface models. It then proposes a good trade-off between model precision and computing efforts for performing complex atrial simulations for a clinical use.

It has been hypothesized that myofiber orientation adapts to achieve a preferred mechanical loading state. To test this hypothesis, a model has been proposed in which myofiber orientation adapts in a response to fiber cross-fiber shear. However, the model lacked active cross-fiber stress that significantly reduces shear amplitudes, according to models of left ventricular (LV) mechanics. Therefore, we included generation of active stress perpendicular to the myofiber direction in an LV mechanics model with shear-induced myofiber reorientation. We tested the effect on fiber orientation, global and local LV function, and shear deformation. The developed pattern of the transverse component in myofiber orientation was similar with and without active cross-fiber stress. Angles of the transverse component were smaller with active cross-fiber stress. In both cases, global and local function increased during restructuring of the LV wall. Amplitudes of circumferential-radial shear strain were decreased after reorientation in both cases, and predicted and measured circumferential-radial shear strain matched better when active cross-fiber stress was included.

Subject-specific, or personalised, modelling is one of the main targets in current cardiac modelling research. The aim of this study is to assess the improvement in predictive power gained by introducing subject-specific fibre models within an electromechanical model of left ventricular contraction in rat. A quantitative comparison of a series of global rule-based fibre models with an image-based locally optimised fibre model was performed. Our results show small difference in the predicted values of ejection fraction, wall thickening and base-to-apex shortening between the fibre models considered. In comparison, much larger differences appear between predicted values and those measured in experimental images. Further study of the constitutive behaviour and architecture of cardiac tissue is required before electromechanical models can fully benefit from the introduction of subject-specific fibres. Additionally, our study shows that, in the current model, an orthotropic description of the tissue is preferable to a transversely isotropic one, for the metrics considered.

Effective electrical size (ratio of ventricular size to electrical activation wavelength) plays a significant role in governing reentrant arrhythmia dynamics. Due to similarities in effective size with the human, the rabbit has been suggested as the most useful experimental model for clinical investigations of fibrillatory arrhythmias. However, how well the effective size of the rabbit, or other small mammalians, correlates to the human during slower pacing rates (such as those often seen during anatomical scar-related reentrant arrhythmias), and importantly how it varies with frequency, is currently not well understood. We used computational ionic ventricular cell models of human, rabbit, rat and guinea pig to investigate interspecies differences in action potential duration, conduction velocity and activation wavelength restitution, and how these combine together to induce important rate-dependant variations in effective size. We conclude that the rabbit model has a closer effective electrical size to the human across a range of activation rates, although differences in effective size dynamics are seen at high frequencies. This suggests potentially important differences in the initiation and anchoring of reentrant waves around anatomical structures, highlighting the need for further investigation of the utility of such models for informing clinical scar-related arrhythmia knowledge.

Because the electrical and mechanical properties of myocardial tissue are strongly anisotropic the local fiber direction is an important parameter for realistic computational models of cardiac excitation and motion. Within the last years Diffusion Tensor Imaging has been established as a noninvasive measuring technique for fiber directions from whole hearts ex vivo. X-ray microtomography offers a much higher spatial resolution than Diffusion Tensor Imaging and could scan a whole heart as well. The inherently low soft-tissue contrast can be enhanced through staining with iodine. We recorded a volumetric scan of a rat heart and filtered the imaging data with a coherence-enhancing anisotropic diffusion filter to enhance its microstructure. The filtering was performed in three dimensions. From the structure tensor of the filtered volumes scalar measurements and fiber tracts were calculated and used for visualization and further analysis.

A novel automatic initialization procedure for left ventricle (LV) cardiac magnetic resonance (CMR) segmentation is proposed through the combination of a LV localization method based on multilevel Otsu thresholding and an elliptical annular template matching algorithm. We then propose to adapt the recent B-spline Explicit Active Surfaces (BEAS) framework to the properties of CMR images by integrating two dedicated energy terms: a weighted localized Chan-Vese region-based energy to explicitly control the equilibrium point between the two regions around each interface and a combined local and global region-based formulation for the myocardial region. The proposed method has been validated on 45 mid-ventricular images taken from the 2009 MICCAI LV segmentation challenge. Results show the efficiency of our method both in terms of shape accuracy and computational times.

Construction of realistic models of the muscle fibers in myocardium is relevant for simulating the electro-mechanical behavior of the heart. Advances in microscopy imaging have improved the potential for visualization of the 3D distribution of myocytes. In this paper, we propose an approach to identify cardiac fibers structures, in multi-photon confocal microscopy images. Our method is based on contrast invariant features such as the multi-scale local phase image, to obtain a tensor representation of the local structure. We show here some results obtained from multi-photon microscopy images acquired in a fetal rabbit heart, where the cardiac microstructure can be extracted from the image in terms of fiber direction as well as fiber compactness. Experiments from phantom data also show a successful application of the proposed methodology.

Heart diseases are a leading cause of death worldwide, making a prompt and accurate diagnosis of cardiac functionality an important task. Recordings of cardiac outflow Doppler velocity profiles, obtained during an echocardiographic examination, are important to quantify hemodynamics and infer cardiac function. For automated segmentation and quantification of these images, a statistical atlas based approach has been proposed previously. Since acquiring a sufficient amount of data for an atlas can be a slow process in clinical practice and possibly result in a small and/or not representative dataset, we present an alternative approach for construction of the statistical atlas. This approach is based on simulating data from virtual patients, using a lumped computational model (CircAdapt), which incorporates knowledge of physiological processes in the human circulatory system under both normal and pathological conditions.

In this paper we address the problem of finding similar coronary angiograms from a database of angiograms using a new constrained nonrigid shape model for the description of coronary arteries. The model captures the non-rigid variations in the artery shapes while still preserving the overall perceptual spatial layout based on the articulation constraints between arteries. Shape matching involves testing for class membership using the constraints specified in the model. The shape similarity method is demonstrated in a similarity retrieval application on a large database of angiogram images.

Mitral valve repair is a complex procedure that requires the ability to predict closed valve shape through the examination of an unpressurized, flaccid valve. These procedures typically include the remodeling of the mitral annulus through the insertion of an annuloplasty ring. While simulations could facilitate the planning of the procedure, traditional finite-element models of mitral annuloplasty are too slow to be clinically feasible and have never been validated in tissue. This work presents a fast method for simulating valve closure post-annuloplasty using a mass-spring tissue model and subject-specific valve geometry. Closed valve shape is predicted in less than one second. The results are validated by implanting an annuloplasty ring in an excised porcine heart and comparing simulated to imaged results. Results indicate that not only can mitral annuloplasty be simulated quickly, but also with sub-millimeter accuracy.

The aim of this work is to study make and break excitation mechanisms elicited by anodal pulses at different coupling intervals using 2D and 3D anisotropic Bidomain simulations. Two different S1-S2 stimulation protocols are considered, one with the S2 pulse delivered at the same location of the S1 pulse and the other at a distant location. Anodal strength-interval (S-I) curves are computed for both S1-S2 protocols showing results consistent with experimental S-I curves, both in terms of stimulus threshold amplitude and depth of the anodal dip during the break phase. The intracellular calcium concentration (

$Ca_i^{2+}$

) distribution presents virtual electrode patterns similar to the transmembrane potential (

V

m

) distribution.

$Ca_i^{2+}$

displays a weak negative change within the virtual anode area, while a strong positive change is observed within the virtual cathode areas. The results show that with both S1-S2 protocols

V

m

and

$Ca_i^{2+}$

exhibit the same make and break excitation mechanisms, but with a delayed

$Ca_i^{2+}$

response.

Intrauterine Growth Restriction due to placental insufficiency leads to cardiac dysfunction in utero which can persist postnatally. Brain sparing by flow redistribution is an adaptive mechanism used by the restricted fetus to ensure delivery of oxygenated blood to the brain. The quantification of reversed flow in the aortic isthmus is used in clinical practice to detect signs of brain sparing. Two parameters are used to quantify reversed flow: pulsatility index and isthmic flow index. We developed a simplified 0-D lumped model of the fetal circulation to simulate brain-sparing for better understanding this compensatory mechanism and its influence on the mentioned parameters. We were able to reproduce the clinical phenomenon and to quantify the effect of brain sparing on pulsatility and isthmic flow indexes. Therefore, our model seems to be a good approximation of the fetal circulation and offers potential to study hemodynamic changes in intrauterine growth restricted fetuses.

In this work, we propose to estimate rule-based myocardial fiber model (RBM) parameters from measured image data, with the goal of personalizing the fiber architecture for cardiac simulations. We first describe the RBM, which is based on a space-dependent angle distribution on the heart surface and then extended to the whole domain through an harmonic lifting of the fiber vectors. We then present a static Unscented Kalman Filter which we use for estimating the degrees of freedom of the fiber model. We illustrate the methodology using noisy synthetic data on a real heart geometry, as well as real DT-MRI-derived fiber data. We also show the impact of different fiber distributions on cardiac contraction simulations.

Heart valves play a very important role in the functioning of the heart and many of the heart failures are related to the valvular dysfunctions, e.g. aortic stenosis and mitral regurgitation. As the medical field is moving towards a patient-specific diagnosis and treatment procedures, modeling of heart valves with patient-specific information is becoming a significant tool in medical field. Here we present the ingredients for valve simulation specifically the aortic valve, with a main focus on a novel spline-based mapping technique which solves many issues in generating patient-specific models – the microstructural mapping, the pre-strain calculations, prescribing dynamic boundary conditions, validation and inverse-modeling to obtain material parameters.

Diffusion tensor magnetic resonance imaging (DTMRI) is usually used to detect the displacement distribution of water molecules in biological structure. However, in post-mortem heart fibre imaging, the low spatial resolution does not allow investigating the cardiac fibre structure at microscopic scale. In this paper, the myocyte arrangement of a human heart is investigated at a high resolution of 3.5

μ

m using the European Synchrotron Radiation Facility (ESRF). The orientation of the myocytes is then computed and extracted at various depths of the heart sample with a multi-scale approach. The helix arrangement of the fibre is obtained at a higher resolution compared to DTMRI. The results show that the measured elevation angles are in good agreement with knowledge of cardiac muscle anatomy. Such high-resolution cardiac fibre orientation information can be used to validate DTMRI measurements and analyze the evolution of cardiac fibre orientations from microscopic level to macroscopic one.

Image guidance of minimally invasive cardiac interventions can be augmented by registering together different imaging modalities. In this paper, we propose a method to combine three modalities: X-ray fluoroscopy, trans-esophageal ultrasound and pre-procedure MRI or CT. The registration of the pre-procedure image involves a potentially unreliable manual initialisation of its position in an X-ray projection view. The method therefore includes an automatic correction using the esophagus location as an additional constraint. We test the method in a phantom experiment and find that initialising the pre-procedure image with up to 9mm offset from its correct position results in a 92% registration success rate. The esophagus constraint improves the capture range in the out-of-plane direction, which simplifies the manual initialisation.

The goal of ECG imaging is the reconstruction of cardiac electrical activities from the potentials measured on the thorax surface. The tool can gain prominent clinical value for diagnosis and pre-interventional planning. The problem is however ill-posed, i.e. it is highly sensitive to modelling and measurement errors. In order to overcome this obstacle a regularization technique must be applied. In this paper we propose a new optimization based method for simultaneous reconstruction of transmembrane voltages and epicardial potentials for localizing the origin of ventricular ectopic beats.

Compared to second-order Tikhonov regularization, the new approach showed superior performance in marking activated regions and provided meaningful results where Tikhonov method failed.

Accurate segmentation of the whole heart from 3D image sequences is an important step in the developement of clinical applications. As manual delineation is a tedious task that is prone to errors and dependant on the expertise of the observer, fully automated segmentation methods are highly desirable. In this work, we present a fully automated method for the segmentation of the whole heart and the great vessels from 3D images. The method is based on a muti-atlas propagation segmentation scheme, that has been proven to be succesful in brain segmentation. Based on a cross correlation metric, our method selects the best atlases for propagation allowing the refinement of the segmentation at each iteration of the propagation. We show that our method allows segmentation from multiple image modalities by validating it on computed tomography angiography (CTA) and magnetic resonance images (MRI). Our results are comparable to state-of-the-art methods on CTA and MRI with average Dice scores of 90.9% and 89.0% for the whole heart when evaluated on a 23 and 8 cases, respectively.

Electrophysiology ablation procedures are performed in an interventional lab. The therapy is delivered through several catheters introduced in cardiac chambers under x-ray guidance. They are also be used to measure some local electrical properties which can be color-coded. A kind of color-map is then established and it can be overlaid to images of the anatomy obtained with fluoroscopy.

A potential improvement in the workflow of the procedure may be reached by tracking the location of the tip of the catheter performing the measurement. We propose here an image-based strategy to detect it and we report the results obtained on a large clinical database. We segment the object of interest by selecting contrasted objects and we characterize them by taking into account all possible co founding factors. A selection strategy has been defined from the distribution of the found values for the true positive and false positive elements in a first clinical database (3000 images from a single site). We got a success rate for the detection of the target object of 86% on a larger database formed of about 4500 images coming from 7 different sites. We also developed an active learning strategy for improving the performance of the algorithm and its stability in the field. The principle is to take into account the user’s manual correction made on a given frame when processing the following ones, which is adapted to the clinical workflow: the segmentation result is assessed and corrected by an operator for each frame. We then gained additional 6% up to 91% on the success rate: the number of algorithm mistakes to be corrected by the operator is reduced to an acceptable level.

Global functional assessment remains a central part of the diagnostic process in daily cardiology practice. Furthermore, real-time 3D echocardiography has been shown to offer superior performance in the assessment of global functional indices, such as stroke volume and ejection fraction, over conventional 2D echo. With this in mind, we present a novel method for tracking the left ventricle (LV) in three-dimensional ultrasound data using a global affine motion model. In order to have a valid region for the underlying assumption of nearly homogeneous motion patterns, we introduce an anatomical region of interest which constrains the global affine motion estimation to a neighborhood around the endocardial surface. This is shown to substantially increase the tracking accuracy and robustness, while simultaneously reducing the required computation time. The proposed anatomical formulation of the optical flow problem is compared with a state-of-the-art real-time tracker and provides competitive performance in the estimation of relevant cardiac volumetric indices used in clinical practice.

ECG imaging is a non-invasive technique of characterizing the electrical activity and the corresponding excitation conduction of the heart using body surface ECG. The method may provide great opportunities in the planning of cardiac interventions and in the diagnosis of cardiac diseases. This work introduces an algorithm for the imaging of transmembrane voltages that is based on a Kalman filter with an augmented measurement model. In the latter, a regularization term is integrated as additional ”measurement”. The filter is trained using

a-priori

-knowledge from a simulation model. Two effects are investigated: the influence of the training data on the reconstruction quality and the representation of

a-priori

knowledge in the trained covariance matrices. The proposed algorithm shows a promising quality of reconstruction and may be used in the future to introduce generic physiological knowledge in solutions of cardiac source imaging.

We propose a methodology to estimate 3D+time maps of left ventricular (LV) fibre strain from human structural and dynamic MRI data. A biomechanical finite element model integrates fibre direction throughout the LV extracted from

ex vivo

human diffusion tensor MRI (DT-MRI) acquisition and motion tracked from tagged MRI (TMRI). This combination enables the estimation of fibre strain and its spatio-temporal variation throughout the cardiac cycle. The sensitivity of fibre strain estimation on the underlying fibre orientation is evaluated using a database of 7 DT-MRI and 1 TMRI datasets acquired on normal subjects. Our analysis indicates that the structure of the LV is designed for maximum homogeneity of fibre strain during ejection.

A clinical image data driven mechanics analysis was used to quantify changes in tissue-specific passive and contractile material properties for groups of normal and HF patients. We have developed an automated mechanics modelling framework to firstly construct left ventricular (LV) mechanics models based on shape information derived from non-invasive dynamic magnetic resonance images, then to characterise passive tissue stiffness and maximum contractile stress by matching the simulated LV mechanics with data from the dynamic cardiac images. Preliminary statistical analysis revealed that patients with hypertrophy or non-ischemic heart failure exhibited increased passive myocardial stiffness compared to the normals. Elevated maximum contractile stress was also observed for hypertrophic patients. Tissue-specific parameter estimation analysis of this kind can potentially be applied in the clinical setting to provide a more specific disease measure to assist with stratification of HF patients.

Experimental recordings of transmural activation in the human left ventricle show variability in recorded activation times. In this paper we demonstrate a framework for fitting the conductivity tensor of a monodomain model to reproduce activation times for an individual experiment whilst taking into account uncertainty in the fibre orientation. By directly registering anatomical features onto the difference between simulated and experimental results, we are then able to identify structural heterogeneities which impact on conduction in the left ventricle.

This manuscript presents a novel, data-driven approach to reduce a detailed cellular model of cardiac myofilament (MF) for efficient and accurate cellular simulations towards cell-to-organ computation. Based on 700 different sarcomere dynamics calculated using Rice model, we show through manifold learning that sarcomere force (SF) dynamics lays surprisingly in a linear manifold despite the non-linear equations of the MF model. Then, we learn a multivariate adaptive regression spline (MARS) model to predict SF from the Rice model parameters and sarcomere length dynamics. Evaluation on 300 testing data showed a prediction error of less than 0.4 nN/mm

2

in terms of maximum force amplitude and 0.87 ms in terms of time to force peak, which is comparable to the differences observed with experimental data. Moreover, MARS provided insights on the driving parameters of the model, mainly MF geometry and cell mechanical passive properties. Thus, our approach may not only constitute a fast and accurate alternative to the original Rice model but also provide insights on parameter sensitivity.

Spatially discordant T-wave alternans (TWA) has been shown to be linked to the genesis of ventricular fibrillation. Identification of discordant TWA through spatial characterization of TWA patterns in the heart has the potential to improve sudden cardiac death risk stratification. In this paper we present a method to solve a new variant of the inverse problem in electrocardiography that is tailored to estimate the TWA regions on the heart from non-invasive measurements on the body surface. We evaluate our method using both body surface potentials synthesized from heart surface potentials generated with ECGSIM and from potentials measured on a canine heart, and we show that this method detects the main regions in the heart undergoing TWA.

With the recent advances in computational power, realistic modeling of heart function within a clinical environment has come into reach. Yet, current modeling frameworks either lack overall completeness or computational performance, and their integration with clinical imaging and data is still tedious. In this paper, we propose an integrated framework to model heart electromechanics from clinical and imaging data, which is fast enough to be embedded in a clinical setting. More precisely, we introduce data-driven techniques for cardiac anatomy estimation and couple them with an efficient GPU (graphics processing unit) implementation of the orthotropic Holzapfel-Ogden model of myocardium tissue, a GPU implementation of a mono-domain electrophysiology model based on the Lattice-Boltzmann method, and a novel method to correctly capture motion during isovolumetric phases. Benchmark experiments conducted on patient data showed that the computation of a whole heart cycle including electrophysiology and biomechanics with mesh resolutions of around 70k elements takes on average 1min 10s on a standard desktop machine (Intel Xeon 2.4GHz, NVIDIA GeForce GTX 580). We were able to compute electrophysiology up to 40.5× faster and biomechanics up to 15.2× faster than with prior CPU-based approaches, which breaks ground towards model-based therapy planning.

Complex 3D beating heart models are now available, but their complexity makes calibration and validation very difficult tasks. We thus propose a systematic approach of deriving simplified reduced-dimensional models, in “0D” – typically, to represent a cardiac cavity, or several coupled cavities – and in “1D” – to model elongated structures such as fibers or myocytes. As illustrations of our approach, we demonstrate model validation based on experiments performed with papillary muscles, and calibration using patient-specific pressure-volume loops.

Body surface potential mapping (BSPM) can be used to non-invasively measure the electrical activity of the heart using a dense set of thorax electrodes and a CT/MR scan of the thorax to solve the inverse problem of electrophysiology (ECGi). This technique now shows potential clinical value for the assessment and treatment of patients with arrhythmias. Co-localisation of the electrode positions and the CT/MR thorax scan is essential. This manuscript describes a method to perform the co-localisation using multiple biplane X-ray images. The electrodes are automatically detected and paired in the X-ray images. Then the 3D positions of the electrodes are computed and mapped onto the thorax surface derived from CT/MR. The proposed method is based on a multi-scale blob detection algorithm and the generalized Hough transform, which can automatically discriminate the leads used for BSPM from other ECG leads. The pairing method is based on epi-polar constraint matching and line pattern detection which assumes that BSPM electrodes are arranged in strips. The proposed methods are tested on a thorax phantom and two clinical cases. Results show an accuracy of 0.33 ± 0.20mm for detecting electrodes in the X-ray images and a success rate of 95.4%. The automatic pairing method achieves a 91.2% success rate.

We present the Adaptive Vector Pattern Matching (AVPM) method, a novel method for the detection of vortical structures specifically designed for velocity encoded 4D PCMRI datasets. AVPM is based on vector pattern matching combined with robust orientation estimation. This combination provides for a simple yet robust algorithm, which is a priori axial flow invariant. We demonstrate these properties by comparing the performance of AVPM with Heiberg’s Vector Pattern Matching algorithm.

We consider a new method to analyse deformation of the myocardial wall from tagging magnetic resonance images. The method exploits the fact that a regular pattern of stripe tags induces a time-dependent frequency covector field tightly coupled to the myocardial tissue and not affected by tag fading. The corresponding local frequency can be disambiguated with the help of the Gabor transform. The transformation of the tagging frequency covector field is governed by the deformation tensor field. Reversely, the deformation (and strain) tensor field can be retrieved from local frequency estimates given at least

n

(independent) tagging sequences, where

n

denotes spatial dimension. For the sake of illustration we consider the conventional case

n

 = 2. Moreover, we make use of an overdetermined system by exploiting 4 instead of 2 tagging directions, which contributes to the robustness of the results. The method does not require explicit knowledge of material motion or tag line extraction. Displacement estimations are compared to HARP.

A spatio-temporal registration procedure of speckle-tracking echocardiography (STE) and cine magnetic resonance (MR) images is presented. It aims to fuse the mechanical information in STE with the tissue information in MR late-gadolinium-enhanced image (LGE-MR), in order to describe the relationship between left ventricular myocardial strain and macroscopic fibrosis in patients with hypertrophic cardiomyopathy (HCM). The registration was performed between the four-chambers-view STE contours (at rest), and the endocardial surfaces of the left ventricle (LV) from cine-MR short-axis-view sequence. The dynamic LV geometries were described by their Fourier descriptors. This spatio-temporal representation of LV geometries was exploited to avoid the lack of dissimilarity between static geometries. To accomplish this goal, the temporal alignment was performed with the dynamical time warping method. The registration was evaluated on four HCM patients with myocardial fibrosis. First results suggest a relationship between myocardial fibrosis and the modification of the strain curve.

The segmentation of three-dimensional microscopic images of cardiac tissues provides important parameters for characterizing cardiac diseases and modeling of tissue function. Segmenting these images is, however, challenging. Currently only time-consuming manual approaches have been developed for this purpose. Here, we introduce an efficient approach for the semi-automatic segmentation (SAS) of cardiomyocytes and the extracellular space in image stacks obtained from confocal microscopy. The approach is based on a morphological watershed algorithm and iterative creation of watershed seed points on a distance map. Results of SAS were consistent with results from manual segmentation (Dice similarity coefficient: 90.8±2.6%). Cell volume was 4.6±6.5% higher in SAS cells, which mainly resulted from cell branches and membrane protrusions neglected by manual segmentation. We suggest that the novel approach constitutes an important tool for characterizing normal and diseased cardiac tissues. Furthermore, the approach is capable of providing crucial parameters for modeling of tissue structure and function.

Different B-spline grid topologies of free-form-deformation (FFD) models have been proposed for 3D strain estimation in echocardiography: classical FFD models are defined in Cartesian space (CFFD), whereas others adapt an anatomically oriented B-spline grid (AFFD) which allow to model the cardiac motion in a more physiological way. The practical advantage of the latter grid topology remains to be proven for echocardiography. In this work, the performance of both models was therefore directly compared using simulated data. Both motion and strain accuracy were competitive for the CFFD and AFFD model: mean error=0.44mm vs 0.48mm, strain error=9.0% vs 7.3% (radial), 2.4% vs 3.1% (longitudinal), 1.9% vs 2.2% (circumferential). However, moving to an anatomical grid topology appears better suited for cardiac deformation estimation as model complexity and computation time was reduced considerably (1051s vs 595s).

Non-rigid image registration has been proposed to extract myocardial motion and deformation from tagged Magnetic Resonance Imaging (t-MRI). Initial efforts focused on finding a set of pairwise registrations, while more recent methods proposed to perform a joint image alignment to exploit temporal information. However, the latter methods usually measure image similarity with respect to the first phase, which may not be optimal due to tag fading. In the present study, we therefore propose a sequential 2D+t registration method exploiting temporal information based on a frame-by-frame image similarity. The method was first tested on synthetic data to fine-tune its parameters, and its applicability was illustrated in human patient data. Furthermore, the sequential 2D+t method was able to detect dysfunctional regions corresponding to delayed-enhanced MRI areas in a database consisting of 8 pig datasets. While differences with respect to traditional 2D methods are limited in terms of end-systolic strain accuracy, including temporal information lead to both smoother trajectories and smoother strain curves.

The heart motion estimation plays an important role in identifying different types of pathology. For this purpose, ultrasound imaging has commonly used due to its high time resolution. In this modality, the speckle is used as a characteristic feature of tissues to improve the accuracy of heart motion estimation which has important clinical implications. However, speckle tracking methods are commonly based on the statistics of speckle, which are affected by the preprocessing steps during the acquisition. This work aims for developing a speckle tracking method for myocardial motion estimation that considers the interpolation step performed to achieve the Cartesian arrangement of the ultrasonic image. The evaluation of the method was carried out using two types of synthetic images and by comparing to other state-of-the-art methods. Results showed that the methods based on speckle features provide a more realistic motion of the heart and follow the natural torsion than others. Besides, the proposed method obtains the best registration performance in most of the deformations tested.

We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (Cine-MRI) data from variational framework. The improved performance of this variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed that the results of proposed method on cine-MRI correlates with the results given by inTag, reference approach on tagged-MRI.

We adapt the formalism of

currents

to compare data surfaces and surfaces of a mechanical model and we use this discrepancy measure to feed a data assimilation procedure. We apply our methodology to perform parameter estimation in a biomechanical model of the heart using synthetic observations of the endo- and epicardium surfaces of an infarcted left ventricle. We compare this formalism with a more classical signed distance operator between surfaces and we numerically show that we have improved the efficiency of our estimation justifying the use of state-of-the-art computational geometry formalism in the data assimilation measurements processing.

The objective of this paper is to assess a previously-proposed

surface-based

electrophysiology model with detailed atrial simulations. This model – derived and substantiated by mathematical arguments – is specifically designed to address thin structures such as atria, and to take into account strong anisotropy effects related to fiber directions with possibly rapid variations across the wall thickness. The simulation results are in excellent adequacy with previous studies, and confirm the importance of anisotropy effects and variations thereof. Furthermore, this surface-based model provides dramatic computational benefits over 3D models with preserved accuracy.

Accelerated methods for acquiring phase contrast (PC) MRI allow the acquisition of 4D flow data of the whole heart in clinically acceptable times. These datasets are becoming interesting both for clinicians – to better stratify diagnosis – and in the modeling community – to constrain patient-specific models. One of the difficulties related to PC data is a limited accuracy in the regions of low flow such as close to the myocardial wall, where the velocity field may even produce observed blood motion across the endocardial surface. To address this issue we propose to constrain the motion of blood in cavity during the analysis by using cine MRI and replacing the PC velocity in the peri-myocardial zone by neighboring tissue velocity obtained by analysis of 3D tagged MRI. We demonstrate the effect of these corrections on 2 healthy volunteer datasets and on one patient with a hypoplastic left ventricle.

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusion-CMR post- processing is to recover tissue impulse-response from observed signal-intensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-response. However this variation influences the accuracy of the deconvolution and introduces possible variations in the results. Using an appropriate order for quantification is essential to allow CMR-perfusion-quantification to develop into a useful clinical tool. The aim of this study was to evaluate the effect of parameter variation in Fermi modelling, autoregressive moving-average model (ARMA), B-spline-basis and exponential-basis deconvolution. Whilst Fermi is the least dependent method on the modelling parameter determination, the B-spline and ARMA were the most sensitive models to this variation. ARMA upon a correct choice of order showed to be the superior to other methods.

In the current study we show how texture mapping to the surface of the heart’s left ventricle(LV) can be used to demonstrate the ventricle’s complex kinematics and highlight impaired regions. The method uses isometric spherical embedding to map a uniform and oriented texture into a reference phase of the LV’s mesh. The texture, attached to the deformed mesh, deforms with it and allows the visualization of rotation, strain and torsion in the circumferential and longitudinal coordinates. Such visualization demonstrates the absolute and relative values of these kinematic parameters and aids in the assessment of regional myocardial function.

Integration of electrical and structural information about substrate in the left ventricle is very important to guide ablation therapies in ventricular tachycardia cases. This integration asks for finding a mapping between electro-anatomical voltage mapping and delay-enhancement magnetic resonance images. We present an evaluation of the accuracy of some mapping strategies, including different standard rigid and non-rigid registration techniques. We also developed a new mapping algorithm to be applied once both geometries are roughly aligned to improve the currently used simple closest point projection. The new mapping algorithm is based on establishing a homeomorphism between both surfaces using a common surface parametrization computed by mesh flattening, then preserving all original information in both modalities. We applied the different mapping strategies to clinical and synthetic data. Results demonstrated a substantial reduction of the point-to-surface error when using the non-rigid registration technique and an improved substrate overlap when using the proposed mapping algorithm.

Computer models, especially those integrated with high resolution 3D surface geometry and myofibre structure throughout the whole atria, are powerful instruments to investigate the mechanism of atrial fibrillation. There are many factors that may contribute to electrical instability of the posterior left atrium (PLA): 1) abrupt changes in wall thickness and myofiber orientations of PLA, 2) different action potential duration in pulmonary vein sleeves and adjacent left atrium (LA) and 3) fibrosis patch. The first two factors have been investigated in our previous work. Here, we further develop an image-based atrial model by incorporating computer-generated fibrosis into the LA to reflect the structure remodeling, motivated by the fibrosis imaging data from Utah. A novel compact finite difference method was implemented to solve the governing cardiac equations. A bursting simulation protocol was applied in the PLA with different levels of structure remodeling and control. Marked conduction delays and uni-directional block were seen with both anisotropy and structure remodeling. The existence of fibrosis increases regional electrophysiological discrepancies and chance of uni-directional block. We conclude that existence of fibrosis potentially increase the occurrences of conduction reentry and the number of wavelets, contributing further to electrical instability in PLA.

The aortic valve owes its strength and durability to a network of collagen fibers within the leaflets. However, the pattern of these fibers and their role in valve function is not well understood. We imaged and quantified the macroscopically visible pattern of collagen fibers in seven porcine aortic valves with particular attention to measuring this pattern in the unstrained leaflet. We then used a structural finite element model of the aortic valve to study the effect of the observed collagen pattern on the configuration of the loaded valve. Results showed that collagen is oriented oblique to the free edge over much of the leaflet in its unstrained state, and simulations suggest that this architecture plays an important role by enabling adequate valve leaflet coaptation. Simulation results were validated by comparison with images of a porcine aortic valve under load.

Promising mitral valve (MV) repair concepts include leaflet augmentation and saddle shaped annuloplasty, and recent long-term studies have indicated that excessive tissue stress and the resulting strain-induced tissue failure are important etiologic factors leading to the recurrence of significant MR after repair. In the present work, we are aiming at developing a high-fidelity computational framework, incorporating detailed collagen fiber architecture, accurate constitutive models for soft valve tissues, and micro-anatomically accurate valvular geometry, for simulations of functional mitral valves which allows us to investigate the organ-level mechanical responses due to physiological loadings. This computational tools also provides a means, with some extension in the future, to help the understanding of the connection between the repair-induced altered stresses/strains and valve functions, and ultimately to aid in the optimal design of MV repair procedure with better performance and durability.

Myofibre orientation plays an important role in electrical activation patterns in the heart. Biophysical models of the whole atria, however, have not yet incorporated realistic myofibre architecture. In this study, we use structure tensor (ST) analysis to determine myofibre orientation from high resolution micro-computed tomography (micro-CT) images and investigate the role of different ST image processing parameters on the computed fibre orientation fields. We found that, although some parameter sets can lead to non-physiological over- or under-smooth fibre fields, a wide range of values produced atrial fibre orientations that are in good agreement with previous anatomical and histological studies. The computed fibre fields can be incorporated in realistic 3D electrophysiological simulations to study electrical propagation under physiological and pathological conditions in the entire atria.

We demonstrate that large legacy databases of manually segmented cardiac MR images can be used to build a shape atlas based on 3D left-ventricular finite-element models. We make use of the Cardiac Atlas Project database to build an atlas of 2,045 asymptomatic cases from the MESA study. Manually placed anatomical landmarks on long-axis and short-axis magnetic resonance images were combined with manually drawn contours on the short axis images which were corrected for breath-hold mis-registration using an automated method. The contours were then fitted by the model using linear least squares optimisation. The fitting error was 0.5 ±0.4 mm at end-diastole and 0.5 ±0.6 mm at end-systole (mean ± std. dev.). Results were validated against 3D models created by experts in a sub-sample of 253 cases using manual breath-hold registration. The atlas surface error was 1.3 ±0.8 mm at end-diastole and 1.2 ±0.9 mm at end-systole. The end-diastolic volume error was 9.0 ±8.7 ml; the end-systolic volume error was 0.8 ±6.3 ml; and the mass error 5.9 ±12.9 g. These differences arose mainly at the base and apex because long-axis images were used in the validation models, but were only used in the automated models to define basal fiducial markers. All models were aligned and scaled, and finally analysed by principal component analysis. Significant differences were found in the first mode shape (sphericity) by gender, smoking, and hypertension.

Studies of intra-species cardiac fiber variability tend to focus on first-order measures such as local fiber orientation. Recent work has shown that myofibers bundle locally into a particular type of minimal surface, the generalized helicoid model (GHM), which is described by three biologically meaningful curvature parameters. In order to allow intra-species comparisons, a typical strategy is to divide the parameters of the generalized helicoid by heart diameter. This normalization does not compensate for variability in myocardial shape between subjects and makes interpretation of intra-species results difficult. This paper proposes to use an atlas of rat and dog myocardium, obtained using diffeomorphic groupwise Log-demons, to register all hearts in a common reference shape to perform the normalization. In this common space the GHM is estimated for all hearts and compared using an improved fitting method. Our results demonstrate improved consistency between GHM curvatures within a species and support a direct relation between myocardial shape and fiber curvature in the heart.

The present paper aims at quantifying the evolution of a given motion pattern under cardiac resynchronization therapy (CRT). It builds upon techniques for population-based cardiac motion quantification (statistical atlases, for inter-sequence spatiotemporal alignment and the definition of normal/abnormal motion). Manifold learning is used on spatiotemporal maps of myocardial motion abnormalities to represent a given abnormal pattern and to compare any individual to that pattern. The methodology was applied to 2D echocardiographic sequences in a 4-chamber view from 108 subjects (21 healthy volunteers and 87 CRT candidates) at baseline, with pacing ON, and at 12 months follow-up. Experiments confirmed that recovery of a normal motion pattern is a necessary but not sufficient condition for CRT response.

A spatiotemporal 3D left ventricular (LV) motion model was developed to extract regional function for intra left ventricular dyssynchrony (intra-LVD). A finite element model was divided into the standard 17 segments. Temporal interpolation was performed by using periodic control theoretic smoothing splines. Intra-LVD was assessed in terms of systolic dyssynchrony index (SDI) by measuring the dispersion of time to reach peak regional ejection fraction (TPREF). We compared two patient groups: 300 asymptomatic (A: 139M/161F, mean age: 61±9.7 yrs) and 105 patients with myocardial infarction (P: 81M/24F, mean age: 63±10.2 yrs). Consistent regional variation in TPREF was observed in the A group, with the basal septal segments having later TPREF. Omitting these segments significantly reduced the SDI values in both groups (

P

 < .001), but improved the statistical differences between cohorts (P: 8.46±4.15% vs A: 6.03±2.93% with basal septal segments; P: 7.24±4.08% vs A: 4.22±2.27% without). The mean Mahalanobis distance of the P group to the distribution of the A group was increased from 2.66±4.63% to 4.99±8.26% (

P

 < .001). These results strongly suggest that basal septal regions should be removed from intra-LVD indices, to provide better discrimination between the two cohorts.

In cardiac MRI, ECG triggering is used or patients are required to hold their breath, to alleviate motion artifacts and deterioration of image quality. However, ECG signal quality is often suboptimal and patients may not be able to adequately hold their breath. Alternative solutions for tracking breathing and cardiac beating can open the way for robust free-breathing and ECG-less cardiac MRI. Herein, we present a novel approach that isolates the effect of breathing, as well as computes both the breathing and cardiac beating waveforms directly from real-time MRI sequences. It turns a challenge into an opportunity to guide the reconstruction of high temporal resolution images. The proposed method is based on a level-set method to segment the left ventricle from a real-time MR sequence collected with free breathing and without ECG triggering. The algorithm extracts an evolving surface area, which captures the heart’s systolic contraction and diastolic expansion in real-time. The computed time series of the heart’s dynamic area is subjected to wavelet analysis, where the breathing and pulsation components are separated. The method was investigated on 12 real-time cardiac MRI acquisitions. We demonstrate that the left ventricular area, as computed by the level set method, produces breathing and cardiac waveforms similar with those extracted by cardiac MR experts (ground-truth). This proof-of-concept work demonstrates the capabilities of the proposed methodology paving the way for incorporation into real-time or retrospective reconstruction of high resolution cardiac MR.

Quantitative motion analysis of the right ventricle (RV) is important to study its function. However, the RV study is more difficult than that of left ventricle (LV) because of its complex shape and the limitations of the existing imaging methods. We propose a diffeomorphic motion estimation method and apply it to the 3D echocardiography of five open-chest pigs under different steady states. We first validate the motion estimation method by using sonomicrometry. Then we estimate the myocardium strain of different steady states. The RV free wall (RVFW) is divided into twelve segments and their strain patterns in each steady states together with the corresponding cardiac mechanics are analyzed. This is the first time to quantitatively analyze the RVFW segment strains from 3D echocardiography by using algorithm.

Given the complex dynamics of cardiac motion, understanding the motion for both normal and pathological cases can aid in understanding how different pathological conditions effect, and are affected by cardiac motion. Naturally, different regions of the left ventricle of the heart move in different ways depending on the location, with significantly different dynamics between the septal and free wall, and basal and apical regions. Therefore, studying the motion at a regional level can provide further information towards identifying abnormal regions for example. The 4D left ventricular motion of a given case was characterised by a low number of parameters at a region level using a cardiac specific polyaffine motion model. The motion was then studied at a regional level by analysing the computed affine transformation matrix of each region. This was used to examine the regional evolution of normal and pathological subjects over the cardiac cycle. The method was tested on 15 healthy volunteers with 4D ground truth landmarks and 5 pathological patients, all candidates for Cardiac Resynchronisation Therapy. Visually significant differences between normal and pathological subjects in terms of synchrony between the regions were obtained, which enables us to distinguish between healthy and unhealthy subjects. The results indicate that the method may be promising for analysing cardiac function.