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Published on: February 13, 2021
CT-Based Simulation of Left Ventricular Hemodynamics: A Pilot Study in Mitral Regurgitation and Left Ventricle
Lukas Obermeier1, Katharina Vellguth1, Adriano Schlief1
1Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Insights
This study introduces a cardiac CT-based computational fluid dynamics (CFD) method to analyze left ventricle (LV) blood flow. The approach is feasible for clinical practice, offering insights into hemodynamics and patient-specific LV function.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Computational Biology
Background:
- Cardiac CT (CCT) excels at anatomical detail but lacks intracardiac flow assessment.
- Computational Fluid Dynamics (CFD) can compute hemodynamics from medical images.
- There is a need to integrate CFD with CCT for comprehensive cardiac analysis.
Purpose of the Study:
- To establish a CCT-based CFD methodology for analyzing left ventricle (LV) hemodynamics.
- To evaluate the clinical feasibility of this computational framework.
- To assess LV hemodynamics in heart failure patients with varying conditions.
Main Methods:
- Utilized multiphase CCT data from 125 heart failure patients.
- Reconstructed LV geometries and ventricular motion from end-diastolic and end-systolic images.
- Computed intraventricular hemodynamics using a prescribed-motion CFD approach.
Main Results:
- Observed disrupted flow patterns and increased energy dissipation in patients with mitral regurgitation (MR).
- Identified regions with impaired intraventricular washout in all cases.
- Demonstrated that CFD analysis is computationally feasible for clinical settings.
Conclusions:
- CCT-based CFD provides patient-specific hemodynamic insights, complementing CCT's anatomical data.
- The method balances accuracy and cost, applicable to standard CCT quality.
- Potential for integration into clinical workflows to aid decision-making and treatment planning.
Background:
Cardiac 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 Methods:
The 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.
Results:
In 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.
Conclusion:
The 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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