Razvan Ioan Ionasec

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.

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.

Dynamic Model-Driven Quantitative and Visual Evaluation of the Aortic Valve from 4D CT

Aortic valve disease is an important cardio-vascular disorder, which affects 2.5% of the global population and often requires elaborate clinical management. Experts agree that visual and quantitative evaluation of the valve, crucial throughout the clinical workflow, is currently limited to 2D imaging which can potentially yield inaccurate measurements. In this paper, we propose a novel approach for morphological and functional quantification of the aortic valve based on a 4D model estimated from computed tomography data. A physiological model of the aortic valve, capable to express large shape variations, is generated using parametric splines together with anatomically-driven topological and geometrical constraints. Recent advances in discriminative learning and incremental searching methods allow rapid estimation of the model parameters from 4D Cardiac CT specifically for each patient. The proposed approach enables precise valve evaluation with model-based dynamic measurements and advanced visualization. Extensive experiments and initial clinical validation demonstrate the efficiency and accuracy of the proposed approach. To the best of our knowledge this is the first time such a patient specific 4D aortic valve model is proposed.

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.

Ultrasound and fluoroscopic images fusion by autonomous ultrasound probe detection

New minimal-invasive interventions such as transcatheter valve procedures exploit multiple imaging modalities to guide tools (fluoroscopy) and visualize soft tissue (transesophageal echocardiography (TEE)). Currently, these complementary modalities are visualized in separate coordinate systems and on separate monitors creating a challenging clinical workflow. This paper proposes a novel framework for fusing TEE and fluoroscopy by detecting the pose of the TEE probe in the fluoroscopic image. Probe pose detection is challenging in fluoroscopy and conventional computer vision techniques are not well suited. Current research requires manual initialization or the addition of fiducials. The main contribution of this paper is autonomous six DoF pose detection by combining discriminative learning techniques with a fast binary template library. The pose estimation problem is reformulated to incrementally detect pose parameters by exploiting natural invariances in the image. The theoretical contribution of this paper is validated on synthetic, phantom and in vivo data. The practical application of this technique is supported by accurate results (< 5 mm in-plane error) and computation time of 0.5s.

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.

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.