ORIGINAL RESEARCH published: 05 July 2022 doi: 10.3389/fcvm.2022.901902 Frontiers in Cardiovascular Medicine | www.frontiersin.org 1 July 2022 | Volume 9 | Article 901902 Edited by: Sabina Gallina, University of Studies G. d’Annunzio Chieti and Pescara, Italy Reviewed by: Rajiv Rampat, William Harvey Hospital, United Kingdom Elias Karabelas, University of Graz, Austria *Correspondence: Natalia Solowjowa solowjowa@dhzb.de †These authors share ﬁrst authorship Specialty section: This article was submitted to Cardiovascular Medicine, a section of the journal Frontiers in Cardiovascular Medicine Received: 22 March 2022 Accepted: 07 June 2022 Published: 05 July 2022 Citation: Goubergrits L, Vellguth K, Obermeier L, Schlief A, Tautz L, Bruening J, Lamecker H, Szengel A, Nemchyna O, Knosalla C, Kuehne T and Solowjowa N (2022) CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics. Front. Cardiovasc. Med. 9:901902. doi: 10.3389/fcvm.2022.901902 CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics Leonid Goubergrits 1,2†, Katharina Vellguth 1†, Lukas Obermeier 1, Adriano Schlief 1, Lennart Tautz 3, Jan Bruening 1, Hans Lamecker 4, Angelika Szengel 4, Olena Nemchyna 5, Christoph Knosalla 5,6,7, Titus Kuehne 1,6 and Natalia Solowjowa 5* 1 Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany, 2 Einstein Center Digital Future, Berlin, Germany, 3 Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, 4 1000shapes, Berlin, Germany, 5 Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany, 6 German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany, 7 Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany Background: Cardiac computed tomography (CCT) based computational ﬂuid dynamics (CFD) allows to assess intracardiac ﬂow features, which are hypothesized as an early predictor for heart diseases and may support treatment decisions. However, the understanding of intracardiac ﬂow is challenging due to high variability in heart shapes and contractility. Using statistical shape modeling (SSM) in combination with CFD facilitates an intracardiac ﬂow analysis. The aim of this study is to prove the usability of a new approach to describe various cohorts. Materials and Methods: CCT data of 125 patients (mean age: 60.6 ± 10.0 years, 16.8% woman) were used to generate SSMs representing aneurysmatic and non- aneurysmatic left ventricles (LVs). Using SSMs, seven group-averaged LV shapes and contraction ﬁelds were generated: four representing patients with and without aneurysms and with mild or severe mitral regurgitation (MR), and three distinguishing aneurysmatic patients with true, intermediate aneurysms, and globally hypokinetic LVs. End-diastolic LV volumes of the groups varied between 258 and 347 ml, whereas ejection fractions varied between 21 and 26%. MR degrees varied from 1.0 to 2.5. Prescribed motion CFD was used to simulate intracardiac ﬂow, which was analyzed regarding large-scale ﬂow features, kinetic energy, washout, and pressure gradients. Results: SSMs of aneurysmatic and non-aneurysmatic LVs were generated. Differences in shapes and contractility were found in the ﬁrst three shape modes. Ninety percent of the cumulative shape variance is described with approximately 30 modes. A comparison of hemodynamics between all groups found shape-, contractility- and MR-dependent differences. Disturbed blood washout in the apex region was found in the aneurysmatic cases. With increasing MR, the diastolic jet becomes less coherent, whereas energy dissipation increases by decreasing kinetic energy.