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.

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.

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.

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.

Cross-modality assessment and planning for pulmonary trunk treatment using CT and MRI imaging.

Congenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting. For advanced evaluation and therapy planning purposes, cardiac Computed Tomography (CT) and cardiac Magnetic Resonance Imaging (cMRI) are important non-invasive investigation techniques with complementary properties. Although, characterized by high temporal resolution, cMRI does not cover the full motion of the pulmonary trunk. The sparse cMRI data acquired in this context include only one 3D scan of the heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. In this paper we present a cross-modality framework for the evaluation of the pulmonary trunk, which combines the advantages of both, cardiac CT and cMRI. A patient-specific model is estimated from both modalities using hierarchical learning-based techniques. The pulmonary trunk model is exploited within a novel dynamic regression-based reconstruction to infer the incomplete cMRI temporal information. Extensive experiments performed on 72 cardiac CT and 74 cMRI sequences demonstrated the average speed of 110 seconds and accuracy of 1.4mm for the proposed approach. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from sparse 4D cMRI data.

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.

Acquisition-related motion compensation for digital subtraction angiography.

Subtraction methods in angiography are generally applied in order to enhance the visualization of blood vessels by eliminating bones and surrounding tissues from X-ray images. The main limitation of these methods is the sensitivity to patient movement, which leads to artifacts and reduces the clinical value of the subtraction images. In this paper we present a novel method for rigid motion compensation with primary application to road mapping, frequently used in image-guided interventions. Using the general concept of image-based registration, we optimize the physical position and orientation of the C-arm X-ray device, thought of as the rigid 3D transformation accounting for the patient movement. The registration is carried out using a hierarchical optimization strategy and a similarity measure based on the variance of intensity differences, which has been shown to be most suitable for fluoroscopic images. Performance evaluation demonstrated the capabilities of the proposed approach to compensate for potential intra-operative patient motion, being more resilient to the fundamental problems of pure image-based registration.

Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from CT data.

Pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data.

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.

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.