Ingmar Voigt

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.

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.

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.

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.

A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot

Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3-D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalization and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.

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.

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.

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.

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.

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.