Computer unterstützte Analyse und Vorhersage des haemodynamischen Ergebnisses von Behandlungsstrategien bei Angeborenen Herzfehlern
Zusammenfassung der Projektergebnisse
The aim of this research project was further development of personalized next generation diagnostic-prognostic tools for patients with congenital heart diseases (CHD) that are based on the innovative combination of imaging science, biofluid mechanics and computer modelling. The interdisciplinary project was carried out by clinicians (German Heart Institute of Berlin,), and biofluid mechanics engineers (Charité – Univesitätsmedizin Berlin, Biofluid Mechanics Laboratory). Both groups are currently joined in frames of a newly founded Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin. During the reported project phase we successfully developed and clinically validated a reduced order 0D model predicting pressure drop caused by a coarctation of the aorta under stress conditions based solely on the acquisition of data under rest conditions. This approach could replace clinically used pharmacological or exercise stress test, which are inaccurate and not available for all patients. The second focus of the project was a development of an aortic valve virtual treatment procedure including a study aiming ti optimize the post-treatment hemodynamic outcome.
Projektbezogene Publikationen (Auswahl)
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Beyond Pressure Gradients: The Effects of Intervention on Heart Power in Aortic Coarctation. PLoS ONE 12(1): 2017, e0168487
Fernandes JF, Goubergrits L, Bruening J, Hellmeier F, Nordmeyer S, da Silva TF, et al.
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Modelbased therapy planning allows prediction of haemodynamic outcome after aortic valve replacement. Scientific Reports 7(1): 2017, 9897
Kelm M, Goubergrits L, Bruening J, Yevtushenko P, Fernandes JF, Sündermann SH, Berger F, Falk V, Kuehne T, CARDIOPROOF group, Nordmeyer S
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Numerical investigation of the impact of branching vessel boundary conditions on aortic hemodynamics. Current Directions in Biomedical Engineering 2017; 3(2): 321–324
Yevtushenko, P., Hellmeier, F., Bruening, J., Kuehne, T., Goubergrits, L.
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Simulation, identification and statistical variation in cardiovascular analysis (SISCA) – A software framework for multi-compartment lumped modeling. Computers in Biology and Medicine 87: 2017, 104-123
Huttary R, Goubergrits L, Schütte Ch, Bernhard S
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CMR-Based and Time-Shift Corrected Pressure Gradients Provide Good Agreement to Invasive Measurements in Aortic Coarctation Journal of the American College of Cardiology: Cardiovascular Imaging: 2018;11(11), 1725-1727
Fernandes JF, Alves R, Silva T, Nordmeyer S, Hellmeier F, Goubergrits L, Hennemuth A, Berger F, Schubert S, Kuehne T, Kelm M
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Hemodynamic Evaluation of a Biological and Mechanical Aortic Valve Prosthesis Using Patient-specific MRI-based CFD. Artificial Organs 42(1): 2018, 49-57
Hellmeier F, Nordmeyer S, Yevtushenko P, Bruening J, Berger F, Kuehne T, Goubergrits L, Kelm M
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Impact of patient-specific LVOT inflow profiles on aortic valve prosthesis and ascending aorta hemodynamics. Journal of Computational Science 24: 2018, 91-100
Bruening J, Hellmeier F, Yevtushenko P, Kelm M, Nordmeyer S, Sündermann SH, Kuehne T, Goubergrits L
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Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD. Cardiovascular Engineering and Technology: 2018 Dec;9(4):582-596
Bruening J, Hellmeier F, Yevtushenko P, Kuehne T, Goubergrits L
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Hemodynamic Changes During Physiological And Pharmacological Stress Testing - A Systematic Review And Meta-Analysis. Frontiers in Cardiovascular Medicine 2019, 6:43
Kilian R, Brosien K, Salcher M, Schubert C, Goubergrits L, Kelle S, Schubert S, Berger F, Kuehne T, Kelm M
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Hodge Decomposition of wall shear stress vector fields characterizing biological flows. Royal Society Open Science: 2019, 6(2): 181970
Razafindrazaka F, Yevtushenko P, Poelke K, Polthier K, Goubergrits L
