I am a postdoc in the group of Alison Marsden, where I focus on cardio­vascular blood flow simulations. As a visiting student researcher with Ellen Kuhl at Stanford, I became fascinated with the application of computer simulations to medical problems. I graduated from the Technical University of Munich with a Ph.D., where I co-founded a group dedicated to the prediction of cardiovascular diseases using simulation methods. Since then, my research mission has been to make simulations more accurate and more accessible for clinicians. During my doctoral studies, we enhanced mechanical models by studying the interaction between the myocardium and the pericardium. We demonstrated how model order reduction could be used to speed up model personalization from patient data, such as cine MRI or blood pressure measurements. We also showed how simulations could enable patient-specific therapy planning of radiofrequency catheter ablation in atrial fibrillation. I am currently working on an NIH-funded project to improve reproducibility in blood flow simulations with data curation methods. We are developing a public repository of patient-specific simulations where other scientists can submit their simulations and automatically regain feedback. My long-term vision is to develop combined physics-based and data-based approaches to enable personalized therapies for the cardiovascular system.

Honors & Awards

  • Dissertation Award, Association of German Engineers (VDI) (2019)
  • Winner Science Slam, Technical University of Munich (2017)
  • Departmental Teaching Award, Technical University of Munich (2017)
  • Departmental Teaching Award, Technical University of Munich (2016)
  • Exchange Scholarship, German Academic Exchange Service (DAAD) (2013)
  • Exchange Scholarship, Prof. Dr.-Ing. Erich Müller-Stiftung (2013)

Professional Education

  • PhD, Technical University of Munich, Mechanical Engineering (2019)
  • MSc, Technical University of Munich, Mechanical Engineering (2013)
  • BSc, Technical University of Munich, Mechanical Engineering (2012)

Stanford Advisors


All Publications

  • Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING Pfaller, M. R., Cruz Varona, M., Lang, J., Bertoglio, C., Wall, W. A. 2020: e3320


    Predictive high-fidelity finite element simulations of human cardiac mechanics commonly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90% of the computation time is spent solving linear systems of equations. We propose to reduce the structural dimension of a monolithically coupled structure-Windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom, while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. Following a brief comparison of subspace interpolation methods, we demonstrate how projection-based model order reduction can be easily integrated into a gradient-based optimization. We demonstrate the performance of our method in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting.

    View details for DOI 10.1002/cnm.3320

    View details for Web of Science ID 000515254400001

    View details for PubMedID 32022424

  • Automatic mapping of atrial fiber orientations for patient-specific modeling of cardiac electromechanics using image registration INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING Hoermann, J. M., Pfaller, M. R., Avena, L., Bertoglio, C., Wall, W. A. 2019; 35 (6): e3190


    Knowledge of appropriate local fiber architecture is necessary to simulate patient-specific electromechanics in the human heart. However, it is not yet possible to reliably measure in vivo fiber directions especially in human atria. Thus, we present a method that defines the fiber architecture in arbitrarily shaped atria using image registration and reorientation methods based on atlas atria with fibers predefined from detailed histological observations. Thereby, it is possible to generate detailed fiber families in every new patient-specific geometry in an automated, time-efficient process. We demonstrate the good performance of the image registration and fiber definition on 10 differently shaped human atria. Additionally, we show that characteristics of the electrophysiological activation pattern that appear in the atlas atria also appear in the patients' atria. We arrive to analogous conclusions for coupled electro-mechano-hemodynamical computations.

    View details for DOI 10.1002/cnm.3190

    View details for Web of Science ID 000471315800008

    View details for PubMedID 30829001

    View details for PubMedCentralID PMC6619047

  • The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling BIOMECHANICS AND MODELING IN MECHANOBIOLOGY Pfaller, M. R., Hoermann, J. M., Weigl, M., Nagler, A., Chabiniok, R., Bertoglio, C., Wall, W. A. 2019; 18 (2): 503–29


    The human heart is enclosed in the pericardial cavity. The pericardium consists of a layered thin sac and is separated from the myocardium by a thin film of fluid. It provides a fixture in space and frictionless sliding of the myocardium. The influence of the pericardium is essential for predictive mechanical simulations of the heart. However, there is no consensus on physiologically correct and computationally tractable pericardial boundary conditions. Here, we propose to model the pericardial influence as a parallel spring and dashpot acting in normal direction to the epicardium. Using a four-chamber geometry, we compare a model with pericardial boundary conditions to a model with fixated apex. The influence of pericardial stiffness is demonstrated in a parametric study. Comparing simulation results to measurements from cine magnetic resonance imaging reveals that adding pericardial boundary conditions yields a better approximation with respect to atrioventricular plane displacement, atrial filling, and overall spatial approximation error. We demonstrate that this simple model of pericardial-myocardial interaction can correctly predict the pumping mechanisms of the heart as previously assessed in clinical studies. Utilizing a pericardial model not only can provide much more realistic cardiac mechanics simulations but also allows new insights into pericardial-myocardial interaction which cannot be assessed in clinical measurements yet.

    View details for DOI 10.1007/s10237-018-1098-4

    View details for Web of Science ID 000461323600018

    View details for PubMedID 30535650

  • An adaptive hybridizable discontinuous Galerkin approach for cardiac electrophysiology INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING Hoermann, J. M., Bertoglio, C., Kronbichler, M., Pfaller, M. R., Chabiniok, R., Wall, W. A. 2018; 34 (5): e2959


    Cardiac electrophysiology simulations are numerically challenging because of the propagation of a steep electrochemical wave front and thus require discretizations with small mesh sizes to obtain accurate results. In this work, we present an approach based on the hybridizable discontinuous Galerkin method (HDG), which allows an efficient implementation of high-order discretizations into a computational framework. In particular, using the advantage of the discontinuous function space, we present an efficient p-adaptive strategy for accurately tracking the wave front. The HDG allows to reduce the overall degrees of freedom in the final linear system to those only on the element interfaces. Additionally, we propose a rule for a suitable integration accuracy for the ionic current term depending on the polynomial order and the cell model to handle high-order polynomials. Our results show that for the same number of degrees of freedom, coarse high-order elements provide more accurate results than fine low-order elements. Introducing p-adaptivity further reduces computational costs while maintaining accuracy by restricting the use of high-order elements to resolve the wave front. For a patient-specific simulation of a cardiac cycle, p-adaptivity reduces the average number of degrees of freedom by 95% compared to the nonadaptive model. In addition to reducing computational costs, using coarse meshes with our p-adaptive high-order HDG method also simplifies practical aspects of mesh generation and postprocessing.

    View details for DOI 10.1002/cnm.2959

    View details for Web of Science ID 000431995600005

    View details for PubMedID 29316340

  • Multiphysics Modeling of the Atrial Systole under Standard Ablation Strategies CARDIOVASCULAR ENGINEERING AND TECHNOLOGY Hoermann, J. M., Bertoglio, C., Nagler, A., Pfaller, M. R., Bourier, F., Hadamitzky, M., Deisenhofer, I., Wall, W. A. 2017; 8 (2): 205–18


    The aim of this study was to develop a computational framework to compare the impact of standard ablation concepts on the mechanical performance of the atria, since different line combinations cannot be applied in practice to the same patient. For this purpuse, we coupled electro-mechano-hemodynamic mathematical models based on biophysical principles and simulate the contractile performance of the atria. We computed systolic pressures and volumes in two patient-specific atrial geometries (one of normal size and one hypertrophied) with various ablation concepts. We found that our computational model is able to detect the differences in the left atrial contractility and ejection fraction for various electrical activation sequences resulting from different ablation line combinations. We show that multiphysics modeling has the potential to quantify the hemodynamic performance of left atria for different ablation lines, which could be used as additional pre-operative clinical information for the choice of the ablation concept in the future.

    View details for DOI 10.1007/s13239-017-0308-z

    View details for Web of Science ID 000402142300008

    View details for PubMedID 28512679

  • On the Role of Mechanics in Chronic Lung Disease MATERIALS Eskandari, M., Pfaller, M. R., Kuhl, E. 2013; 6 (12): 5639-5658


    Progressive airflow obstruction is a classical hallmark of chronic lung disease, affecting more than one fourth of the adult population. As the disease progresses, the inner layer of the airway wall grows, folds inwards, and narrows the lumen. The critical failure conditions for airway folding have been studied intensely for idealized circular cross-sections. However, the role of airway branching during this process is unknown. Here, we show that the geometry of the bronchial tree plays a crucial role in chronic airway obstruction and that critical failure conditions vary significantly along a branching airway segment. We perform systematic parametric studies for varying airway cross-sections using a computational model for mucosal thickening based on the theory of finite growth. Our simulations indicate that smaller airways are at a higher risk of narrowing than larger airways and that regions away from a branch narrow more drastically than regions close to a branch. These results agree with clinical observations and could help explain the underlying mechanisms of progressive airway obstruction. Understanding growth-induced instabilities in constrained geometries has immediate biomedical applications beyond asthma and chronic bronchitis in the diagnostics and treatment of chronic gastritis, obstructive sleep apnea and breast cancer.

    View details for DOI 10.3390/ma6125639

    View details for Web of Science ID 000330297600014

    View details for PubMedCentralID PMC5452755

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