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CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics
Leonid Goubergrits1,2, Katharina Vellguth1, Lukas Obermeier1
1Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Insights
This study introduces a new method combining cardiac computed tomography (CCT) with computational fluid dynamics (CFD) and statistical shape modeling (SSM) to analyze intracardiac blood flow. The approach reveals how heart shape and function impact blood flow, aiding in diagnosing heart diseases.
Area of Science:
- Cardiovascular Imaging and Hemodynamics
- Computational Biology and Medical Simulation
Background:
- Intracardiac flow analysis is crucial for early heart disease detection and treatment planning.
- High variability in heart shape and contractility presents challenges in understanding intracardiac flow.
- Statistical Shape Modeling (SSM) combined with Computational Fluid Dynamics (CFD) offers a potential solution for analyzing complex intracardiac flow patterns.
Purpose of the Study:
- To demonstrate the usability of a novel approach integrating CCT, CFD, and SSM for analyzing intracardiac hemodynamics across diverse patient cohorts.
- To investigate the impact of left ventricular (LV) aneurysm and mitral regurgitation (MR) on intracardiac flow characteristics.
Main Methods:
- Generation of SSMs from CCT data of 125 patients, representing aneurysmatic and non-aneurysmatic LVs.
- Creation of seven group-averaged LV shapes and contraction fields based on aneurysm status, MR severity, and LV contractility.
- Simulation of intracardiac flow using prescribed motion CFD, analyzing features like kinetic energy, washout, and pressure gradients.
Main Results:
- SSMs captured significant variations in LV shape and contractility, with approximately 30 modes describing 90% of the shape variance.
- Hemodynamic analysis revealed shape-, contractility-, and MR-dependent differences in blood flow.
- Disturbed apical blood washout was observed in aneurysmatic cases, with globally hypokinetic LVs showing the poorest overall washout.
Conclusions:
- The CCT-based CFD and SSM approach shows promise for facilitating intracardiac flow analysis, enhancing the diagnostic value of CCT.
- This methodology has the potential to be integrated into clinical workflows to support diagnostic and treatment decisions for heart diseases.
- Further computational enhancements could lead to widespread clinical adoption and improved patient care.
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. The poorest blood washout was found for the globally hypokinetic group, whereas the weakest blood washout in the apex region was found for the true aneurysm group.
Conclusion:
The proposed CCT-based analysis of hemodynamics combining CFD with SSM seems promising to facilitate the analysis of intracardiac flow, thus increasing the value of CCT for diagnostic and treatment decisions. With further enhancement of the computational approach, the methodology has the potential to be embedded in clinical routine workflows and support clinicians.
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