A CFD study on the interplay of torsion and vortex guidance by the mitral valve on the left ventricular wash-out making use of overset meshes (Chimera technique)

Abstract

Cardiovascular disease often occurs with silent and gradual alterations of cardiac blood flow that can lead to the onset of chronic pathological conditions. Image-based patient-specific Computational Fluid Dynamics (CFD) models allow for an extensive quantification of the flow field beyond the direct capabilities of medical imaging techniques that could support the clinicians in the early diagnosis, follow-up, and treatment planning of patients. Nonetheless, the large and impulsive kinematics of the left ventricle (LV) and the mitral valve (MV) pose relevant modeling challenges. Arbitrary Lagrangian-Eulerian (ALE) based computational fluid dynamics (CFD) methods struggle with the complex 3D mesh handling of rapidly moving valve leaflets within the left ventricle (LV). We, therefore, developed a Chimera-based (overset meshing) method to build a patient-specific 3D CFD model of the beating LV which includes a patient-inspired kinematic model of the mitral valve (LVMV). Simulations were performed with and without torsion. In addition, to evaluate how the intracardiac LV flow is impacted by the MV leaflet kinematics, a third version of the model without the MV was generated (LV with torsion). For all model versions, six cardiac cycles were simulated. All simulations demonstrated cycle-to-cycle variations that persisted after six cycles but were albeit marginal in terms of the magnitude of standard deviation of velocity and vorticity which may be related to the dissipative nature of the numerical scheme used. The MV was found to have a crucial role in the development of the intraventricular flow by enhancing the direct flow, the apical washout, and the propagation of the inlet jet towards the apical region. Consequently, the MV is an essential feature in the patient-specific CFD modeling of the LV. The impact of torsion was marginal on velocity, vorticity, wall shear stress, and energy loss, whereas it resulted to be significant in the evaluation of particle residence times. Therefore, including torsion could be considered in patient-specific CFD models of the LV, particularly when aiming to study stasis and residence time. We conclude that, despite some technical limitations encountered, the Chimera technique is a promising alternative for ALE methods for 3D CFD models of the heart that include the motion of valve leaflets.

Verification Study of In Silico Computed Intracardiac Blood Flow With 4D Flow MRI

“…The sequences are performed quickly, and the registration can be largely automated. When drawing comparison to other studies, segmenting the LV based on cine short axis images and the MV via rotations of the long axis view passing through apex and the MV center are found to be common strategies [3], [33], [34]. Comparing the shape reconstructions to PC-MRI measurements yields matching results in terms of volume flow rates.…”

“…6). The out-of-plane vorticity ω z is computed via spatial deviation of the velocity v according to (3).…”

“…They are based on different imaging modalities and numerical methods. Some approaches furthermore address high fidelity modeling in the context of multi-physic considerations [11]- [13] or detailed modeling of anatomical structures [3], [14], [15]. Despite the quantity of methods, clinical applications are still mostly distant.…”

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks

“…Furthermore, technological advancements during the last 2 decades have facilitated the use of image-based computational fluid dynamics (CFD) analysis within the field of cardiovascular medicine ( Morris et al, 2016 ). This approach, known as image-based CFD modeling of patient-specific hemodynamics, allows the computation of flow parameters with notably higher spatial and temporal resolutions than achievable by any existing in vivo imaging technique ( Canè et al, 2022 ). The outcomes of these simulations could finally be used for clinical support, particularly in cardiovascular surgical planning and diagnostics ( Morris et al, 2016 ).…”