Aortic and Mitral Valve Modeling from Multi-Modal Image Data - Voigt, Ingmar; et al. (Medical Image Recognition, Segmentation and Parsing, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.aortic

Automatic Detection and Quantification of Mitral Regurgitation on TTE with Application to Assist Mitral Clip Planning and Evaluation - Vitanovski, Dime; Voigt, Ingmar; et al. (Proceedings of the MICCAI Workshop on Clinical Image-based Procedures: From Planning to Intervention, 2012)

Automatic Image-to-Model Framework for Patient-specific Electromechanical Modeling of the Heart - Neumann, Dominik; Tommaso, Mansi; Grbic, Sasa; Voigt, Ingmar; Georgescu, Bogdan; Kayvanpour, Elham; Amr, Ali; Sedaghat-Hamedani, Farbod; Haas, Jan; Katus, Hugo; Meder, Benjamin; Hornegger, Joachim; et al. (2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa_6

Complete Valvular Heart Apparatus Model from 4D Cardiac CT - Vitanovski, Dime; Voigt, Ingmar; et al. (Proceedings of 13th International Conference on Medical Image Computing and Computer Assisted Intervention, 2010)

Computational decision support for percutaneous aortic valve implantation - Voigt, Ingmar; Ionasec, Razvan; Georgescu, Bogdan; Boese, Jan; Brockmann, Gernot; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2010)
Valve replacement is the most common therapy for diseased aortic valves. Percutaneous approaches are becoming increasingly popular, due to reduced procedural complications and lower follow-up rates. Still there is a lack of efficient tools for valve quantification and preoperative simulation of replacement and repair procedures. Thus the success of the intervention relies to a large portion on experience and skills of the operator. In this paper we propose a novel framework for preoperative planning, intraoperative guidance and post-operative assessment of percutaneous aortic valve replacement procedures with stent mounted devices. A comprehensive model of the aortic valvular complex including aortic valve and aorta ascendens is estimated with fast and robust learning-based techniques from cardiac CT images. Consequently our model is used to perform a in-silico delivery of the valve implant based on deformable simplex meshes and geometrical constraints. The predictive power of the model-based in-silico valve replacement was validated on 3D cardiac CT data from 20 patients through comparison of preoperative prediction against postoperatively imaged real device. In our experiments the method performed with an average accuracy of 2.18 mm and a speed of 55 seconds. To the best of our knowledge, this is the first time a computational framework is validated using real pre- and postoperative patient data. © 2010 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.comput

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease - Voigt, Ingmar; Vitanovski, Dime; Ionasec, Razvan Ioan; Tsymbal, Alexey; Georgescu, Bogdan; Zhou, S. Kevin; Huber, Martin; Navab, Nassir; Hornegger, Joachim; Comaniciu, Dorin (Medical Imaging 2010: Image Processing, 2010)
Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Model-based Postoperative Modeling of Stent-Based Devices from CT: Application to TAVI - Voigt, Ingmar; Assoumou Mengue, Etienne; Ionasec, Razvan; Tommaso, Mansi; Bernhardt, Dominik; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (1st International MICCAI-Workshop on Computer Assisted Stenting, 2012)

Model-driven physiological assessment of the mitral valve from 4D TEE - Voigt, Ingmar; Ionasec, Razvan Ioan; Georgescu, Bogdan; Houled, Helene; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
Disorders of the mitral valve are second most frequent, cumulating 14 percent of total number of deaths caused by Valvular Heart Disease each year in the United States and require elaborate clinical management. Visual and quantitative evaluation of the valve is an important step in the clinical workflow according to experts as knowledge about mitral morphology and dynamics is crucial for interventional planning.1, 2 Traditionally this involves examination and metric analysis of 2D images comprising potential errors being intrinsic to the method. Recent commercial solutions are limited to specific anatomic components, pathologies and a single phase of cardiac 4D acquisitions only. This paper introduces a novel approach for morphological and functional quantification of the mitral valve based on a 4D model estimated from ultrasound data. A physiological model of the mitral valve, covering the complete anatomy and eventual shape variations, is generated utilizing parametric spline surfaces constrained by topological and geometrical prior knowledge. The 4D model's parameters are estimated for each patient using the latest discriminative learning and incremental searching techniques. Precise evaluation of the anatomy using model-based dynamic measurements and advanced visualization are enabled through the proposed approach in a reliable, repeatable and reproducible manner.3 The efficiency and accuracy of the method is demonstrated through experiments and an initial validation based on clinical research results. To the best of our knowledge this is the first time such a patient specific 4D mitral valve model is proposed, covering all of the relevant anatomies and enabling to model the common pathologies at once. © 2009 SPIE.

Patient-specific modeling of the heart: Applications to cardiovascular disease management - Ionasec, Razvan; Voigt, Ingmar; Mihalef, Viorel; Grbic, Sasa; Vitanovski, Dime; Wang, Yabin; Zheng, Yefeng; Hornegger, Joachim; Navab, Nassir; Georgescu, Bogdan; Comaniciu, Dorin (Springer-verlag, 2010)
As decisions in cardiology increasingly rely on non-invasive methods, fast and precise image analysis tools have become a crucial component of the clinical workflow. Especially when dealing with complex cardiovascular disorders, such as valvular heart disease, advanced imaging techniques have the potential to significantly improve treatment outcome as well as to reduce procedure risks and related costs. We are developing patient-specific cardiac models, estimated from available multi-modal images, to enable advanced clinical applications for the management of cardiovascular disease. In particular, a novel physiological model of the complete heart, including the chambers and valvular apparatus is introduced, which captures a large spectrum of morphological, dynamic and pathological variations. To estimate the patient-specific model parameters from four-dimensional cardiac images, we have developed a robust learning-based framework. The model-driven approach enables a multitude of advanced clinical applications. Gold standard clinical methods, which manually process 2D images, can be replaced with fast, precise, and comprehensive model-based quantification to enhance cardiac analysis. For emerging percutaneous and minimal invasive valve interventions, cardiac surgeons and interventional cardiologists can substantially benefit from automated patient selection and virtual valve implantation techniques. Furthermore, the complete cardiac model enables for patient-specific hemodynamic simulations and blood flow analysis. Extensive experiments demonstrated the potential of these technologies to improve treatment of cardiovascular disease. © 2010 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.patien

Patient-Specific Model of Left Heart Anatomy, Dynamics and Hemodynamics from 4D TEE: A First Validation Study - Voigt, Ingmar; Mansi, Tommaso; Mihalef, Viorel; Ionasec, Razvan; Calleja, Anna; Assoumou Mengue, Etienne; Sharma, Puneet; Houle, Helene; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
Patient-specific models of the heart physiology have become powerful instruments able to improve the diagnosis and treatment of cardiac disease. A systemic representation of the whole organ is required to capture the complex functional and hemodynamical interdependencies among the anatomical structures. We propose a novel framework for personalized modeling of the left-side heart that integrates comprehensive data of the morphology, function and hemodynamics. Patient-specific fluid dynamics are computed over the entire cardiac cycle using embedded boundary and ghost fluid methods, constrained by the dynamics of highly detailed anatomical models. Personalized boundary conditions are determined by estimating cardiac shape and motion from 4D TEE images through robust discriminative learning methods. Qualitative and quantitative validation of the computed blood dynamics is performed against Doppler echocardiography measurements, following an original methodology. Results showed a high agreement between simulation and ground truth and a correlation of r = 0.85 (p < 0.0002675). To the best of our knowledge, this is the first time that computational fluid dynamics are simulated on a systemic and comprehensive patient-specific model of the heart and validated against routinely acquired clinical ground truth. © 2011 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.patien

Personalized Mitral Valve Closure Computation and Uncertainty Analysis from 3D Echocardiography - Voigt, Ingmar; Neumann, Dominik; et al. (Medical Image Analysis, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.person

Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT - Ionasec, Razvan; Voigt, Ingmar; Georgescu, Bogdan; Wang, Yabin; Houle, Helene; Hornegger, Joachim; Navab, Nassir; Comaniciu, Dorin (Springer-verlag, 2009)
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 1.45mm 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. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.person_6

Robust Heart Valve Modeling and Advanced Computational Decision Support - Voigt, Ingmar (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)
Valvular heart disease (VHD) is a major public health problem with a prevalence of 2.5% in the US. Tens of thousands of minimally invasive procedures are performed every year and new therapeutical approaches are continuously developed at a rapid pace. Major clinical decisions and therapies are nowadays guided by peri- and intraoperative imaging with an increasing emphasis on 3D. Given the complexity of pathology and therapies in VHD, there is a strong need for fast, precise and reproducible quantification. To this end, several contributions are proposed in this thesis across various aspects of VHD management. These are structured into two parts: i) Mitral valve (MV) modeling and ii) advanced computational decision support. The first part starts with describing a comprehensive MV model obtained from 3D+t transesophageal echocardiography (TEE). An original methodology combines robust machine learning (ML) techniques and biomechanical constraints to obtain a temporally consistent estimate of model parameters. Extensive experiments on a large data set of 195 3D+t TEE volumes – comprising 3026 3D frames – demonstrate, that this automatic method is highly competitive in terms of speed and accuracy and outperforms purely data-driven methods in terms of robustness. Clinical evaluation in numerous centers around the world shows the applicability of its versatile quantification capabilities to various aspects of disease characterization and therapy planning. Subsequently a more lightweight alternative enables for robust 3D tracking of the MV annulus at high frame rates. Two parallel components based on ML and image-based tracking complement each other in robustness and speed. Experiments with emulated probe motion suggest suitability towards an interventional setting for guiding transcatheter mitral valve repair (TMVR) procedures. The second part proposes computational approaches for advanced decision support throughout the clinical workflow. First, the previously described valve modeling is combined with chamber models into a holistic and detailed model of the left heart. This enables for estimating patient-specific computational hemodynamics by serving as boundary condition for a level-set based computational fluid dynamics (CFD) solver. A validation concept using clinically acquired Doppler measurements is proposed and shows high agreement. Finally, a framework is presented for post-operative modeling of self-expandable stent devices for monitoring in transcatheter aortic valve implantation (TAVI) procedures. The technique is based on deformable simplex meshes, geometrical constraints and ML. Evaluation on postoperative computed tomography (CT) data shows promising accuracy.<br
Keywords: Cardiac Imaging; Heart Valves; Machine Learning

Robust physically-constrained modeling of the mitral valve and subvalvular apparatus - Voigt, Ingmar; Tommaso, Mansi; Ionasec, Razvan; Assoumou Mengue, Etienne; Houle, Helene; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
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 5.1mm at a speed of 9sec, 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. © 2011 Springer-Verlag.

Superior Reproducibility of Automated 3-D Surgical Anatomy of Normal and Abnormal Mitral Valve when Compared to a Manual Approach - Voigt, Ingmar; et al. (American College of Cardiology Annual Scientific Session & Expo, 2011)

Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation - Voigt, Ingmar; et al. (Medical Image Analysis, 2017)
UnivIS-Import:2017-01-09:Pub.2017.tech.IMMD.IMMD5.toward
Keywords: Finite element biomechanical model; Mitral valve; Mitral valve closure simulation; Patient-specific model; Transesophageal echocardiography