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Liu X, Guo G, Wang A, Wang Y, Chen S, Zhao P, Yin Z, Liu S, Gao Z, Zhang H, Zu L. Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography. Comput Biol Med 2024; 177:108608. [PMID: 38796880 DOI: 10.1016/j.compbiomed.2024.108608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/20/2024] [Accepted: 05/11/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND AND OBJECTIVE Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value. METHODS Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG. RESULTS The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from -27.7 to 18.7, outperforming MPGEcho, which ranged from -40.1 to 18.0. CONCLUSIONS The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.
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Affiliation(s)
- Xiujian Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Ge Guo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Anbang Wang
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Yupeng Wang
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China
| | - Shaomin Chen
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China
| | - Penghui Zhao
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China
| | - Zhaowei Yin
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China
| | - Suxuan Liu
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China
| | - Zhifan Gao
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Lingyun Zu
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China; NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Peking University, Beijing, China; Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China.
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Govindarajan V, Kolanjiyil A, Wanna C, Kim H, Prakash S, Chandran KB, McPherson DD, Johnson NP. Biomechanical Evaluation of Aortic Valve Stenosis by Means of a Virtual Stress Test: A Fluid-Structure Interaction Study. Ann Biomed Eng 2024; 52:414-424. [PMID: 37957528 DOI: 10.1007/s10439-023-03389-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 10/15/2023] [Indexed: 11/15/2023]
Abstract
The impact of aortic valve stenosis (AS) extends beyond the vicinity of the narrowed leaflets into the left ventricle (LV) and into the systemic vasculature because of highly unpredictable valve behavior and complex blood flow in the ascending aorta that can be attributed to the strong interaction between the narrowed cusps and the ejected blood. These effects can become exacerbated during exercise and may have implications for disease progression, accurate diagnosis, and timing of intervention. In this 3-D patient-specific study, we employ strongly coupled fluid-structure interaction (FSI) modeling to perform a comprehensive biomechanical evaluation of systolic ejection dynamics in a stenosed aortic valve (AV) during increasing LV contraction. Our model predictions reveal that the heterogeneous ∆P vs. Q relationship that was observed in our previous clinical study can be attributed to a non-linear increase (by ~ 1.5-fold) in aortic valve area as LV heart rate increases from 70 to 115 bpm. Furthermore, our results show that even for a moderately stenotic valve, increased LV contraction during exercise can lead to high-velocity flow turbulence (Re = 11,700) in the aorta similar to that encountered with a severely stenotic valve (Re ~ 10,000), with concomitant greater viscous loss (~3-fold increase) and elevated wall stress in the ascending aorta. Our FSI predictions also reveal that individual valve cusps undergo distinct and highly non-linear increases (>100%) in stress during exercise, potentially contributing to progressive calcification. Such quantitative biomechanical evaluations from realistic FSI workflows provide insights into disease progression and can be integrated with current stress testing for AS patients to comprehensively predict hemodynamics and valve function under both baseline and exercise conditions.
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Affiliation(s)
- Vijay Govindarajan
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA.
- Boston Children's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | | | - Charles Wanna
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
| | - Hyunggun Kim
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
- Sungkyunkwan University, Suwon, Gyeonggi, Korea
| | - Siddharth Prakash
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
| | - Krishnan B Chandran
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
- The University of Iowa, Iowa City, IA, USA
| | - David D McPherson
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
| | - Nils P Johnson
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX, 77054, USA
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Verstraeten S, Hoeijmakers M, Tonino P, Brüning J, Capelli C, van de Vosse F, Huberts W. Generation of synthetic aortic valve stenosis geometries for in silico trials. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3778. [PMID: 37961993 DOI: 10.1002/cnm.3778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 11/15/2023]
Abstract
In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid-structure-interaction models to perform in silico trials.
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Affiliation(s)
- Sabine Verstraeten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Pim Tonino
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jan Brüning
- Institute of Computer-assisted Cardiovascular Medicine, Charite Universitaetsmedizin, Berlin, Germany
| | - Claudio Capelli
- Institute of Cardiovascular Science, University College London, London, UK
| | - Frans van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Goubergrits L, Schafstedde M, Cesarovic N, Szengel A, Schmitt B, Wiegand M, Romberg J, Arndt A, Kuehne T, Brüning J. CT-based comparison of porcine, ovine, and human pulmonary arterial morphometry. Sci Rep 2023; 13:20211. [PMID: 37980386 PMCID: PMC10657407 DOI: 10.1038/s41598-023-47532-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 11/14/2023] [Indexed: 11/20/2023] Open
Abstract
To facilitate pre-clinical animal and in-silico clinical trials for implantable pulmonary artery pressure sensors, understanding the respective species pulmonary arteries (PA) anatomy is important. Thus, morphological parameters describing PA of pigs and sheep, which are common animal models, were compared with humans. Retrospective computed tomography data of 41 domestic pigs (82.6 ± 18.8 kg), 14 sheep (49.1 ± 6.9 kg), and 49 patients (76.8 ± 18.2 kg) were used for reconstruction of the subject-specific PA anatomy. 3D surface geometries including main, left, and right PA as well as LPA and RPA side branches were manually reconstructed. Then, specific geometric parameters (length, diameters, taper, bifurcation angle, curvature, and cross-section enlargement) affecting device implantation and post-interventional device effect and efficacy were automatically calculated. For both animal models, significant differences to the human anatomy for most geometric parameters were found, even though the respective parameters' distributions also featured relevant overlap. Out of the two animal models, sheep seem to be better suitable for a preclinical study when considering only PA morphology. Reconstructed geometries are provided as open data for future studies. These findings support planning of preclinical studies and will help to evaluate the results of animal trials.
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Affiliation(s)
- Leonid Goubergrits
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Marie Schafstedde
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Nikola Cesarovic
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | | | - Boris Schmitt
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Moritz Wiegand
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | | | | | - Titus Kuehne
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Jan Brüning
- Deutsches Herzzentrum Der Charité (DHZC), Institute of Computer-Assisted Cardiovascular Medicine, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
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Franke B, Schlief A, Walczak L, Sündermann S, Unbehaun A, Kempfert J, Solowjowa N, Kühne T, Goubergrits L. Comparison of hemodynamics in biological surgical aortic valve replacement and transcatheter aortic valve implantation: An in-silico study. Artif Organs 2023; 47:352-360. [PMID: 36114598 DOI: 10.1111/aor.14405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/03/2022] [Accepted: 08/30/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES In aortic valve replacement (AVR), the treatment strategy as well as the model and size of the implanted prosthesis have a major impact on the postoperative hemodynamics and thus on the clinical outcome. Preinterventional prediction of the hemodynamics could support the treatment decision. Therefore, we performed paired virtual treatment with transcatheter AVR (TAVI) and biological surgical AVR (SAVR) and compared hemodynamic outcomes using numerical simulations. METHODS 10 patients with severe aortic stenosis (AS) undergoing TAVI were virtually treated with both biological SAVR and TAVI to compare post-interventional hemodynamics using numerical simulations of peak-systolic flow. Virtual treatment procedure was done using an in-house developed tool based on position-based dynamics methodology, which was applied to the patient's anatomy including LVOT, aortic root and aorta. Geometries were automatically segmented from dynamic CT-scans and patient-specific flow rates were calculated by volumetric analysis of the left ventricle. Hemodynamics were assessed using the STAR CCM+ software by solving the RANS equations. RESULTS Virtual treatment with TAVI resulted in realistic hemodynamics comparable to echocardiographic measurements (median difference in transvalvular pressure gradient [TPG]: -0.33 mm Hg). Virtual TAVI and SAVR showed similar hemodynamic functions with a mean TPG with standard deviation of 8.45 ± 4.60 mm Hg in TAVI and 6.66 ± 3.79 mm Hg in SAVR (p = 0.03) while max. Wall shear stress being 12.6 ± 4.59 vs. 10.2 ± 4.42 Pa (p = 0.001). CONCLUSIONS Using the presented method for virtual treatment of AS, we were able to reliably predict post-interventional hemodynamics. TAVI and SAVR show similar hemodynamics in a pairwise comparison.
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Affiliation(s)
- Benedikt Franke
- Institute of Computer-assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Adriano Schlief
- Institute of Computer-assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars Walczak
- Institute of Computer-assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Fraunhofer MEVIS, Bremen, Germany
| | - Simon Sündermann
- Department of Cardiology and Angiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Axel Unbehaun
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Jörg Kempfert
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Natalia Solowjowa
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Titus Kühne
- Institute of Computer-assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Leonid Goubergrits
- Institute of Computer-assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Einstein Center Digital Future, Berlin, Germany
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Yevtushenko P, Goubergrits L, Franke B, Kuehne T, Schafstedde M. Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine. Front Cardiovasc Med 2023; 10:1136935. [PMID: 36937926 PMCID: PMC10020717 DOI: 10.3389/fcvm.2023.1136935] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.
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Affiliation(s)
- Pavlo Yevtushenko
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Leonid Goubergrits
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Benedikt Franke
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Titus Kuehne
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Marie Schafstedde
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- *Correspondence: Marie Schafstedde,
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Obermeier L, Vellguth K, Schlief A, Tautz L, Bruening J, Knosalla C, Kuehne T, Solowjowa N, Goubergrits L. CT-Based Simulation of Left Ventricular Hemodynamics: A Pilot Study in Mitral Regurgitation and Left Ventricle Aneurysm Patients. Front Cardiovasc Med 2022; 9:828556. [PMID: 35391837 PMCID: PMC8980692 DOI: 10.3389/fcvm.2022.828556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/03/2022] [Indexed: 12/30/2022] Open
Abstract
BackgroundCardiac CT (CCT) is well suited for a detailed analysis of heart structures due to its high spatial resolution, but in contrast to MRI and echocardiography, CCT does not allow an assessment of intracardiac flow. Computational fluid dynamics (CFD) can complement this shortcoming. It enables the computation of hemodynamics at a high spatio-temporal resolution based on medical images. The aim of this proposed study is to establish a CCT-based CFD methodology for the analysis of left ventricle (LV) hemodynamics and to assess the usability of the computational framework for clinical practice.Materials and MethodsThe methodology is demonstrated by means of four cases selected from a cohort of 125 multiphase CCT examinations of heart failure patients. These cases represent subcohorts of patients with and without LV aneurysm and with severe and no mitral regurgitation (MR). All selected LVs are dilated and characterized by a reduced ejection fraction (EF). End-diastolic and end-systolic image data was used to reconstruct LV geometries with 2D valves as well as the ventricular movement. The intraventricular hemodynamics were computed with a prescribed-motion CFD approach and evaluated in terms of large-scale flow patterns, energetic behavior, and intraventricular washout.ResultsIn the MR patients, a disrupted E-wave jet, a fragmentary diastolic vortex formation and an increased specific energy dissipation in systole are observed. In all cases, regions with an impaired washout are visible. The results furthermore indicate that considering several cycles might provide a more detailed view of the washout process. The pre-processing times and computational expenses are in reach of clinical feasibility.ConclusionThe proposed CCT-based CFD method allows to compute patient-specific intraventricular hemodynamics and thus complements the informative value of CCT. The method can be applied to any CCT data of common quality and represents a fair balance between model accuracy and overall expenses. With further model enhancements, the computational framework has the potential to be embedded in clinical routine workflows, to support clinical decision making and treatment planning.
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Affiliation(s)
- Lukas Obermeier
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Lukas Obermeier
| | - Katharina Vellguth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Adriano Schlief
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lennart Tautz
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jan Bruening
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Knosalla
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Titus Kuehne
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Department of Congenital Heart Disease, German Heart Center Berlin, Berlin, Germany
| | - Natalia Solowjowa
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Leonid Goubergrits
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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