Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation.

Transesophageal echocardiography (TEE) is routinely used to provide important qualitative and quantitative information regarding mitral regurgitation. Contemporary planning of surgical mitral valve repair, however, still relies heavily upon subjective predictions based on experience and intuition. While patient-specific mitral valve modeling holds promise, its effectiveness is limited by assumptions that must be made about constitutive material properties. In this paper, we propose and develop a semi-automated framework that combines machine learning image analysis with geometrical and biomechanical models to build a patient-specific mitral valve representation that incorporates image-derived material properties. We use our computational framework, along with 3D TEE images of the open and closed mitral valve, to estimate values for chordae rest lengths and leaflet material properties. These parameters are initialized using generic values and optimized to match the visualized deformation of mitral valve geometry between the open and closed states. Optimization is achieved by minimizing the summed Euclidean distances between the estimated and image-derived closed mitral valve geometry. The spatially varying material parameters of the mitral leaflets are estimated using an extended Kalman filter to take advantage of the temporal information available from TEE. This semi-automated and patient-specific modeling framework was tested on 15 TEE image acquisitions from 14 patients. Simulated mitral valve closures yielded average errors (measured by point-to-point Euclidean distances) of 1.86 ± 1.24 mm. The estimated material parameters suggest that the anterior leaflet is stiffer than the posterior leaflet and that these properties vary between individuals, consistent with experimental observations described in the literature.

Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography.

Intervention planning is essential for successful Mitral Valve (MV) repair procedures. Finite-element models (FEM) of the MV could be used to achieve this goal, but the translation to the clinical domain is challenging. Many input parameters for the FEM models, such as tissue properties, are not known. In addition, only simplified MV geometry models can be extracted from non-invasive modalities such as echocardiography imaging, lacking major anatomical details such as the complex chordae topology. A traditional approach for FEM computation is to use a simplified model (also known as parachute model) of the chordae topology, which connects the papillary muscle tips to the free-edges and select basal points. Building on the existing parachute model a new and comprehensive MV model was developed that utilizes a novel chordae representation capable of approximating regional connectivity. In addition, a fully automated personalization approach was developed for the chordae rest length, removing the need for tedious manual parameter selection. Based on the MV model extracted during mid-diastole (open MV) the MV geometric configuration at peak systole (closed MV) was computed according to the FEM model. In this work the focus was placed on validating MV closure computation. The method is evaluated on ten in vitro ovine cases, where in addition to echocardiography imaging, high-resolution µCT imaging is available for accurate validation.

Four-dimensional modelling of the mitral valve by real-time 3D transoesophageal echocardiography: proof of concept.

OBJECTIVES: The complexity of the mitral valve (MV) anatomy and function is not yet fully understood. Assessing the dynamic movement and interaction of MV components to define MV physiology during the complete cardiac cycle remains a challenge. We herein describe a novel semi-automated 4D MV model. METHODS: The model applies quantitative analysis of the MV over a complete cardiac cycle based on real-time 3D transoesophageal echocardiography (RT3DE) data. RT3DE data of MVs were acquired for 18 patients. The MV annulus and leaflets were semi-automatically reconstructed. Dimensions of the mitral annulus (anteroposterior and anterolateral-posteromedial diameter, annular circumference, annular area) and leaflets (MV orifice area, intercommissural distance) were acquired. Variability and reproducibility (intraclass correlation coefficient, ICC) for interobserver and intraobserver comparison were quantified at 4 time points during the cardiac cycle (mid-systole, end-systole, mid-diastole and end-diastole). RESULTS: Mitral annular dimensions provided highly reliable and reproducible measurements throughout the cardiac cycle for interobserver (variability range, 0.5-1.5%; ICC range, 0.895-0.987) and intraobserver (variability range, 0.5-1.6%; ICC range, 0.827-0.980) comparison, respectively. MV leaflet parameters showed a high reliability in the diastolic phase (variability range, 0.6-9.1%; ICC range, 0.750-0.986), whereas MV leaflet dimensions showed a high variability and lower correlation in the systolic phase (variability range, 0.6-22.4%; ICC range, 0.446-0.915) compared with the diastolic phase. CONCLUSIONS: This 4D model provides detailed morphological reconstruction as well as sophisticated quantification of the complex MV structure and dynamics throughout the cardiac cycle with a precision not yet described.

New concepts for mitral valve imaging.

The high complexity of the mitral valve (MV) anatomy and function is not yet fully understood. Studying especially the dynamic movement and interaction of MV components to describe MV physiology during the cardiac cycle remains a challenge. Imaging is the key to assessing details of MV disease and to studying the lesion and dysfunction of MV according to Carpentier. With the advances of computational geometrical and biomechanical MV models, improved quantification and characterization of the MV has been realized. Geometrical models can be divided into rigid and dynamic models. Both models are based on reconstruction techniques of echocardiographic or computed tomographic data sets. They allow detailed analysis of MV morphology and dynamics throughout the cardiac cycle. Biomechanical models aim to simulate the biomechanics of MV to allow for examination and analysis of the MV structure with blood flow. Two categories of biomechanical MV models can be distinguished: structural models and fluid-structure interaction (FSI) models. The complex structure and dynamics of MV apparatus throughout the cardiac cycle can be analyzed with different types of computational models. These represent substantial progress in the diagnosis of structural heart disease since MV morphology and dynamics can be studied in unprecedented detail. It is conceivable that MV modeling will contribute significantly to the understanding of the MV.

Automated quantitative 3-dimensional modeling of the aortic valve and root by 3-dimensional transesophageal echocardiography in normals, aortic regurgitation, and aortic stenosis: comparison to computed tomography in normals and clinical implications.

BACKGROUND: We tested the ability of a novel automated 3-dimensional (3D) algorithm to model and quantify the aortic root from 3D transesophageal echocardiography (TEE) and computed tomographic (CT) data. METHODS AND RESULTS: We compared the quantitative parameters obtained by automated modeling from 3D TEE (n=20) and CT data (n=20) to those made by 2D TEE and targeted 2D from 3D TEE and CT in patients without valve disease (normals). We also compared the automated 3D TEE measurements in severe aortic stenosis (n=14), dilated root without aortic regurgitation (n=15), and dilated root with aortic regurgitation (n=20). The automated 3D TEE sagittal annular diameter was significantly greater than the 2D TEE measurements (P=0.004). This was also true for the 3D TEE and CT coronal annular diameters (P<0.01). The average 3D TEE and CT annular diameter was greater than both their respective 2D and 3D sagittal diameters (P<0.001). There was no significant difference in 2D and 3D measurements of the sinotubular junction and sinus of valsalva diameters (P>0.05) in normals, but these were significantly different (P<0.05) in abnormals. The 3 automated intercommissural distance and leaflet length and height did not show significant differences in the normals (P>0.05), but all 3 were significantly different compared with the abnormal group (P<0.05). The automated 3D annulus commissure coronary ostia distances in normals showed significant difference between 3D TEE and CT (P<0.05); also, these parameters by automated 3D TEE were significantly different in abnormal (P<0.05). Finally, the automated 3D measurements showed excellent reproducibility for all parameters. CONCLUSIONS: Automated quantitative 3D modeling of the aortic root from 3D TEE or CT data is technically feasible and provides unique data that may aid surgical and transcatheter interventions.

Computational modelling of the right ventricle in repaired tetralogy of Fallot: can it provide insight into patient treatment?

AIMS: Pulmonary regurgitation (PR) causes progressive right ventricle (RV) dilatation and dysfunction in repaired tetralogy of Fallot (rToF). Declining RV function is often insidious and the timing of pulmonary valve replacement remains under debate. Quantifying the pathophysiology of adverse RV remodelling due to worsening PR may help in defining the best timing for pulmonary valve replacement. Our aim was to identify whether complex three-dimensional (3D) deformations of RV shape, as assessed with computer modelling, could constitute an anatomical biomarker that correlated with clinical parameters in rToF patients. METHODS AND RESULTS: We selected 38 rToF patients (aged 10-30 years) who had complete data sets and had not undergone PVR from a population of 314 consecutive patients recruited in a collaborative study of four hospitals. All patients underwent cardiovascular magnetic resonance (CMR) imaging: PR and RV end-diastolic volumes were measured. An unbiased shape analysis framework was used with principal component analysis and linear regression to correlate shape with indexed PR volume. Regurgitation severity was significantly associated with RV dilatation (P = 0.01) and associated with bulging of the outflow tract (P = 0.07) and a dilatation of the apex (P = 0.08). CONCLUSION: In this study, we related RV shape at end-diastole to clinical metrics of PR in rToF patients. By considering the entire 3D shape, we identified a link between PR and RV dilatation, outflow tract bulging, and apical dilatation. Our study constitutes a first attempt to correlate 3D RV shape with clinical metrics in rToF, opening new ways to better quantify 3D RV change in rToF.

Image-based computational models for TAVI planning: from CT images to implant deployment.

Transcatheter aortic valve implantation (TAVI) is becoming the standard choice of care for non-operable patients suffering from severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, accurate preoperative planning is crucial for a successful outcome. The most important decision during planning is selecting the proper implant type and size. Due to the wide variety in device sizes and types and non-circular annulus shapes, there is often no obvious choice for the specific patient. Most clinicians base their final decision on their previous experience. As a first step towards a more predictive planning, we propose an integrated method to estimate the aortic apparatus from CT images and compute implant deployment. Aortic anatomy, which includes aortic root, leaflets and calcifications, is automatically extracted using robust modeling and machine learning algorithms. Then, the finite element method is employed to calculate the deployment of a TAVI implant inside the patient-specific aortic anatomy. The anatomical model was evaluated on 198 CT images, yielding an accuracy of 1.30 +/- 0.23 mm. In eleven subjects, pre- and post-TAVI CT images were available. Errors in predicted implant deployment were of 1.74 +/- 0.40 mm in average and 1.32 mm in the aortic valve annulus region, which is almost three times lower than the average gap of 3 mm between consecutive implant sizes. Our framework may thus constitute a surrogate tool for TAVI planning.

An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: application to MitralClip intervention planning.

Treatment of mitral valve (MV) diseases requires comprehensive clinical evaluation and therapy personalization to optimize outcomes. Finite-element models (FEMs) of MV physiology have been proposed to study the biomechanical impact of MV repair, but their translation into the clinics remains challenging. As a step towards this goal, we present an integrated framework for finite-element modeling of the MV closure based on patient-specific anatomies and boundary conditions. Starting from temporal medical images, we estimate a comprehensive model of the MV apparatus dynamics, including papillary tips, using a machine-learning approach. A detailed model of the open MV at end-diastole is then computed, which is finally closed according to a FEM of MV biomechanics. The motion of the mitral annulus and papillary tips are constrained from the image data for increased accuracy. A sensitivity analysis of our system shows that chordae rest length and boundary conditions have a significant influence upon the simulation results. We quantitatively test the generalization of our framework on 25 consecutive patients. Comparisons between the simulated closed valve and ground truth show encouraging results (average point-to-mesh distance: 1.49 ± 0.62 mm) but also the need for personalization of tissue properties, as illustrated in three patients. Finally, the predictive power of our model is tested on one patient who underwent MitralClip by comparing the simulated intervention with the real outcome in terms of MV closure, yielding promising prediction. By providing an integrated way to perform MV simulation, our framework may constitute a surrogate tool for model validation and therapy planning.

Complete valvular heart apparatus model from 4D cardiac CT.

The cardiac valvular apparatus, composed of the aortic, mitral, pulmonary and tricuspid valves, is an essential part of the anatomical, functional and hemodynamic characteristics of the heart and the cardiovascular system as a whole. Valvular heart diseases often involve multiple dysfunctions and require joint assessment and therapy of the valves. In this paper, we propose a complete and modular patient-specific model of the cardiac valvular apparatus estimated from 4D cardiac CT data. A new constrained Multi-linear Shape Model (cMSM), conditioned by anatomical measurements, is introduced to represent the complex spatio-temporal variation of the heart valves. The cMSM is exploited within a learning-based framework to efficiently estimate the patient-specific valve parameters from cine images. Experiments on 64 4D cardiac CT studies demonstrate the performance and clinical potential of the proposed method. Our method enables automatic quantitative evaluation of the complete valvular apparatus based on non-invasive imaging techniques. In conjunction with existent patient-specific chamber models, the presented valvular model enables personalized computation modeling and realistic simulation of the entire cardiac system.

Robust physically-constrained modeling of the mitral valve and subvalvular apparatus.

Mitral valve (MV) is often involved in cardiac diseases, with various pathological patterns that require a systemic view of the entire MV apparatus. Due to its complex shape and dynamics, patient-specific modeling of the MV constitutes a particular challenge. We propose a novel approach for personalized modeling of the dynamic MV and its subvalvular apparatus that ensures temporal consistency over the cardiac sequence and provides realistic deformations. The idea is to detect the anatomical MV components under constraints derived from the biomechanical properties of the leaflets. This is achieved by a robust two-step alternate algorithm that combines discriminative learning and leaflet biomechanics. Extensive evaluation on 200 transesophageal echochardiographic sequences showed an average Hausdorff error of 5.1 mm at a speed of 9 sec, which constitutes an improvement of up to 11.5% compared to purely data driven approaches. Clinical evaluation on 42 subjects showed, that the proposed fully-automatic approach could provide discriminant biomarkers to detect and quantify remodeling of annulus and leaflets in functional mitral regurgitation.

Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac CT images.

There is a growing need for patient-specific and holistic modelling of the heart to support comprehensive disease assessment and intervention planning as well as prediction of therapeutic outcomes. We propose a patient-specific model of the whole human heart, which integrates morphology, dynamics and haemodynamic parameters at the organ level. The modelled cardiac structures are robustly estimated from four-dimensional cardiac computed tomography (CT), including all four chambers and valves as well as the ascending aorta and pulmonary artery. The patient-specific geometry serves as an input to a three-dimensional Navier-Stokes solver that derives realistic haemodynamics, constrained by the local anatomy, along the entire heart cycle. We evaluated our framework with various heart pathologies and the results correlate with relevant literature reports.

Complete valvular heart apparatus model from 4D cardiac CT.

The cardiac valvular apparatus, composed of the aortic, mitral, pulmonary and tricuspid valve, is an essential part of the anatomical, functional and hemodynamic mechanism of the heart and the cardiovascular system as a whole. Valvular heart diseases often involve multiple dysfunctions and require joint assessment and therapy of the valves. In this paper, we propose a complete and modular patient-specific model of the cardiac valvular apparatus estimated from 4D cardiac CT data. A new constrained Multi-linear Shape Model (cMSM), conditioned by anatomical measurements, is introduced to represent the complex spatiotemporal variation of the heart valves. The cMSM is exploited within a learning-based framework to efficiently estimate the patient-specific valve parameters from cine images. Experiments on 64 4D cardiac CT studies demonstrate the performance and clinical potential of the proposed method. To the best of our knowledge, it is the first time cardiologists and cardiac surgeons can benefit from an automatic quantitative evaluation of the complete valvular apparatus based on non-invasive imaging techniques. In conjunction with existent patient-specific chamber models, the presented valvular model enables personalized computation modeling and realistic simulation of the entire cardiac system.

Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE.

As decisions in cardiology increasingly rely on noninvasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best of our knowledge, we propose the first automatic system for patient-specific modeling and quantification of the left heart valves, which operates on cardiac computed tomography (CT) and transesophageal echocardiogram (TEE) data. Robust algorithms, based on recent advances in discriminative learning, are used to estimate patient-specific parameters from sequences of volumes covering an entire cardiac cycle. A novel physiological model of the aortic and mitral valves is introduced, which captures complex morphologic, dynamic, and pathologic variations. This holistic representation is hierarchically defined on three abstraction levels: global location and rigid motion model, nonrigid landmark motion model, and comprehensive aortic-mitral model. First we compute the rough location and cardiac motion applying marginal space learning. The rapid and complex motion of the valves, represented by anatomical landmarks, is estimated using a novel trajectory spectrum learning algorithm. The obtained landmark model guides the fitting of the full physiological valve model, which is locally refined through learned boundary detectors. Measurements efficiently computed from the aortic-mitral representation support an effective morphological and functional clinical evaluation. Extensive experiments on a heterogeneous data set, cumulated to 1516 TEE volumes from 65 4-D TEE sequences and 690 cardiac CT volumes from 69 4-D CT sequences, demonstrated a speed of 4.8 seconds per volume and average accuracy of 1.45 mm with respect to expert defined ground-truth. Additional clinical validations prove the quantification precision to be in the range of inter-user variability. To the best of our knowledge this is the first time a patient-specific model of the aortic and mitral valves is automatically estimated from volumetric sequences.

A statistical model of right ventricle in tetralogy of Fallot for prediction of remodelling and therapy planning.

Patients with repaired Tetralogy of Fallot commonly suffer from chronic pulmonary valve regurgitations and extremely dilated right ventricle (RV). To reduce risk factors, new pulmonary valves must be re-implanted. However, establishing the best timing for re-intervention is a clinical challenge because of the large variability in RV shape and in pathology evolution. This study aims at quantifying the regional impacts of growth and regurgitations upon the end-diastolic RV anatomy. The ultimate goal is to determine, among clinical variables, predictors for the shape in order to build a statistical model that predicts RV remodelling. The proposed approach relies on a forward model based on currents and LDDMM algorithm to estimate an unbiased template of 18 patients and the deformations towards each individual shape. Cross-sectional multivariate analyses are carried out to assess the effects of body surface area, tricuspid and transpulmonary valve regurgitations upon the RV shape, The statistically significant deformation modes were found clinically relevant. Canonical correlation analysis yielded a generative model that was successfully tested on two new patients.

Personalized modeling and assessment of the aortic-mitral coupling from 4D TEE and CT.

The anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. An integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. In this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. A holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left heart valves. Robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional TEE and CT data. The piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. Consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. Finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. Experiments performed on 65 4D TEE and 69 4D CT sequences demonstrated an average accuracy of 1.45 mm and speed of 60 seconds for the proposed approach. Initial clinical validation on model-based and expert measurement showed the precision to be in the range of the inter-user variability. To the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from TEE and CT data is proposed.

Calibration of laryngeal endoscopic high-speed image sequences by an automated detection of parallel laser line projections.

High-speed laryngeal endoscopic systems record vocal fold vibrations during phonation in real-time. For a quantitative analysis of vocal fold dynamics a metrical scale is required to get absolute laryngeal dimensions of the recorded image sequence. For the clinical use there is no automated and stable calibration procedure up to now. A calibration method is presented that consists of a laser projection device and the corresponding image processing for the automated detection of the laser calibration marks. The laser projection device is clipped to the endoscope and projects two parallel laser lines with a known distance to each other as calibration information onto the vocal folds. Image processing methods automatically identify the pixels belonging to the projected laser lines in the image data. The line detection bases on a Radon transform approach and is a two-stage process, which successively uses temporal and spatial characteristics of the projected laser lines in the high-speed image sequence. The robustness and the applicability are demonstrated with clinical endoscopic image sequences. The combination of the laser projection device and the image processing enables the calibration of laryngeal endoscopic images within the vocal fold plane and thus provides quantitative metrical data of vocal fold dynamics.