Effect of membrane insertion for tricuspid regurgitation using immersed-boundary lattice Boltzmann method

Fluid-structure interaction modeling of compliant aortic valves using the lattice Boltzmann CFD and FEM methods

The lattice Boltzmann method (LBM) has been increasingly used as a stand-alone CFD solver in various biomechanical applications. This study proposes a new fluid-structure interaction (FSI) co-modeling framework for the hemodynamic-structural analysis of compliant aortic valves. Toward that goal, two commercial software packages are integrated using the lattice Boltzmann (LBM) and finite element (FE) methods. The suitability of the LBM-FE hemodynamic FSI is examined in modeling healthy tricuspid and bicuspid aortic valves (TAV and BAV), respectively. In addition, a multi-scale structural approach that has been employed explicitly recognizes the heterogeneous leaflet tissues and differentiates between the collagen fiber network (CFN) embedded within the elastin matrix of the leaflets. The CFN multi-scale tissue model is inspired by monitoring the distribution of the collagen in 15 porcine leaflets. Different simulations have been examined, and structural stresses and resulting hemodynamics are analyzed. We found that LBM-FE FSI approach can produce good predictions for the flow and structural behaviors of TAV and BAV and correlates well with those reported in the literature. The multi-scale heterogeneous CFN tissue structural model enhances our understanding of the mechanical roles of the CFN and the elastin matrix behaviors. The importance of LBM-FE FSI also emerges in its ability to resolve local hemodynamic and structural behaviors. In particular, the diastolic fluctuating velocity phenomenon near the leaflets is explicitly predicted, providing vital information on the flow transient nature. The full closure of the contacting leaflets in BAV is also demonstrated. Accordingly, good structural kinematics and deformations are captured for the entire cardiac cycle.

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

BACKGROUND

Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography.

OBJECTIVES

To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.

METHOD

We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied.

RESULT

The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively.

CONCLUSION

The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.

Biomechanical effects of the novel series LVAD on the aortic valve

BACKGROUND AND OBJECTIVE

The series type of LVAD (i.e., BJUT-II VAD) is a novel left ventricular assist device, whose effects on the aortic valve remain unclear.

METHODS

The biomechanical effects of BJUT-II VAD on the aortic valve were investigated by using a fluid-structure interaction method. The geometric model of BJUT-II VAD was virtually implanted into the ascending aorta to generate the realistic flow pattern for the aortic valve (i.e., support). In addition, the biomechanical states of the aortic valve without BJUT-II VAD support was computed as control (i.e., control case).

RESULTS

Results demonstrated that the biomechanical effects of BJUT-II VAD were quite different from that resulting from traditional "bypass LVAD." Compared with those in the control case, BJUT-II VAD support could significantly reduce the stress load of the leaflet (maximum stress, 0.5 MPa in the control case vs. 0.12 MPa in the support case). Similarly, the rapid valve opening time (100 ms in the control case vs. 175 ms in the support case) and rapid valve closing time (50 ms in the control case vs. 150 ms in the support case) in the support case were obviously longer than those in the control case. Moreover, BJUT-II VAD support reduced retrograde blood flow during the diastolic phase and significantly changed the distribution of WSS of the leaflets.

CONCLUSIONS

In summary, while unloading the left ventricle, BJUT-II VAD could provide beneficial biomechanical states for the aortic leaflets, thereby reducing the risk of aortic valve disease.