This study's objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based ...on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced WSS and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of the average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.
Implantable left ventricular assist devices (LVADs) became the therapy of choice in treating end‐stage heart failure. Although survival improved substantially and is similar in currently clinically ...implanted LVADs HeartMate II (HM II) and HeartWare HVAD, complications related to blood trauma are frequently observed. The aim of this study was to compare these two pumps regarding their potential blood trauma employing computational fluid dynamics. High‐resolution structured grids were generated for the pumps. Newtonian flow was calculated, solving Reynolds‐averaged Navier–Stokes equations with a sliding mesh approach and a k‐ω shear stress transport turbulence model for the operating point of 4.5 L/min and 80 mm Hg. The pumps were compared in terms of volumes subjected to certain viscous shear stress thresholds, below which no trauma was assumed (von Willebrand factor cleavage: 9 Pa, platelet activation: 50 Pa, and hemolysis: 150 Pa), and associated residence times. Additionally, a hemolysis index was calculated based on a Eulerian transport approach. Twenty‐two percent of larger volumes above 9 Pa were observed in the HVAD; above 50 Pa and 150 Pa the differences between the two pumps were marginal. Residence times were higher in the HVAD for all thresholds. The hemolysis index was almost equal for the HM II and HVAD. Besides the gap regions in both pumps, the inlet regions of the rotor and diffuser blades have a high hemolysis production in the HM II, whereas in the HVAD, the volute tongue is an additional site for hemolysis production. Thus, in this study, the comparison of the HM II and the HVAD using numerical methods indicated an overall similar tendency to blood trauma in both pumps. However, influences of turbulent shear stresses were not considered and effects of the pivot bearing in the HM II were not taken into account. Further in vitro investigations are required.
The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics ...assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-of-concept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23 mmHg) outperforms the 1D BRNN (median RMSE of 4.25 mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03 mmHg) compared to the 3D CNN (±8.91 mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model's training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model's accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic
non-gothic) does not improve ANN performance for test cases sharing the same shape.
Purpose
Computational fluid dynamics (CFD)-based calculation of intranasal airflow became an important method in rhinologic research. Current evidence shows weak to moderate correlation as well as a ...systematic underprediction of nasal resistance by numerical simulations. In this study, we investigate whether these differences can be explained by measurement uncertainties caused by rhinomanometric devices and procedures. Furthermore, preliminary findings regarding the impact of tissue movements are reported.
Methods
A retrospective sample of 17 patients, who reported impaired nasal breathing and for which rhinomanometric (RMM) measurements using two different devices as well as computed tomography scans were available, was investigated in this study. Three patients also exhibited a marked collapse of the nasal valve. Agreement between both rhinomanometric measurements as well as between rhinomanometry and CFD-based calculations was assessed using linear correlation and Bland–Altman analyses. These analyses were performed for the volume flow rates measured at trans-nasal pressure differences of 75 and 150 Pa during inspiration and expiration.
Results
The correlation between volume flow rates measured using both RMM devices was good (
R
2
> 0.72 for all breathing states), and no relevant differences in measured flow rates was observed (21.6 ml/s and 14.8 ml/s for 75 and 150 Pa, respectively). In contrast, correlation between RMM and CFD was poor (
R
2
< 0.5) and CFD systematically overpredicted RMM-based flow rate measurements (231.8 ml/s and 328.3 ml/s). No differences between patients with and without nasal valve collapse nor between inspiration and expiration were observed.
Conclusion
Biases introduced during RMM measurements, by either the chosen device, the operator or other aspects as for example the nasal cycle, are not strong enough to explain the gross differences commonly reported between RMM- and CFD-based measurement of nasal resistance. Additionally, tissue movement during breathing is most likely also no sufficient explanation for these differences.
In patients with aortic coarctation it would be desirable to assess pressure gradients as well as information about blood flow profiles at rest and during exercise. We aimed to assess the hemodynamic ...responses to physical exercise by combining MRI-ergometry with computational fluid dynamics (CFD). MRI was performed on 20 patients with aortic coarctation (13 men, 7 women, mean age 21.5 ± 13.7 years) at rest and during ergometry. Peak systolic pressure gradients, wall shear stress (WSS), secondary flow degree (SFD) and normalized flow displacement (NFD) were calculated using CFD. Stroke volume was determined based on MRI. On average, the pressure gradient was 18.0 ± 16.6 mmHg at rest and increased to 28.5 ± 22.6 mmHg (p < 0.001) during exercise. A significant increase in cardiac index was observed (p < 0.001), which was mainly driven by an increase in heart rate (p < 0.001). WSS significantly increased during exercise (p = 0.006), whereas SFD and NFD remained unchanged. The combination of MRI-ergometry with CFD allows assessing pressure gradients as well as flow profiles during physical exercise. This concept has the potential to serve as an alternative to cardiac catheterization with pharmacological stress testing and provides hemodynamic information valuable for studying the pathophysiology of aortic coarctation.
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.
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).
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.
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.
Abstract Platelet deposition to collagen-coated surface under low shear conditions was investigated using an experimental model. The flow chamber was created by combining a stationary and a ...rotational glass plates spaced 50 μm apart. Blood filled into this space was subjected to a simple Couette flow. Both glass plates were covered with albumin to render them anti-thrombogenic. However, one spot 1×1 mm in size was covered with collagen. This spot was where the platelets deposited. The device was mounted on an inverted microscope and the platelet deposition was recorded. Platelets were dyed to render them fluorescent. The blood used was human blood from healthy volunteers. It was subjected to a range of low shear rates (below 700 1/s) to find out how they act on platelet deposition. The results show a characteristic curve with elevated platelet deposition in the range of 150 1/s. For the interpretation of these results a numerical model was developed. It applies the Monte Carlo method to model a random walk of platelets. This diffusive motion was superimposed on the convective motion by the Couette flow. A satisfactory match to the experimental data was achieved.
Abstract This study compared pressure fields by 4-dimensional (4D), velocity-encoded cine (VEC) cardiac magnetic resonance imaging (CMR) with pressures measured by the clinical gold standard ...catheterization. Thirteen patients (n = 7 male, n = 6 female) with coarctation were studied. The 4D-VEC-CMR pressure fields were computed by solving the Pressure-Poisson equation. The agreement between catheterization and CMR-based methods was determined at 5 different measurement sites along the aorta. For all sites, the correlation coefficients between measures varied between 0.86 and 0.97 (p < 0.001). The Bland-Altman test showed good agreement between peak systolic pressure gradients across the coarctation. The nonsignificant (p > 0.2) bias was +2.3 mm Hg (± 6.4 mm Hg, 2 SDs) for calibration with dynamic pressures and +1.5 mm Hg (± 4.6 mm Hg, 2 SDs) for calibration with static pressure. In a clinical setting of coarctation, pressure fields can be accurately computed from 4D-VEC-CMR–derived flows. In patients with coarctation, this noninvasive technique might evolve to an alternative to invasive catheterization.
Computational fluid dynamics (CFD) models of blood flow in the left ventricle (LV) and aorta are important tools for analyzing the mechanistic links between myocardial deformation and flow patterns. ...Typically, the use of image-based kinematic CFD models prevails in applications such as predicting the acute response to interventions which alter LV afterload conditions. However, such models are limited in their ability to analyze any impacts upon LV load or key biomarkers known to be implicated in driving remodeling processes as LV function is not accounted for in a mechanistic sense. This study addresses these limitations by reporting on progress made toward a novel electro-mechano-fluidic (EMF) model that represents the entire physics of LV electromechanics (EM) based on first principles. A biophysically detailed finite element (FE) model of LV EM was coupled with a FE-based CFD solver for moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two clinical cases of patients suffering from aortic coarctations (CoA) were built and parameterized based on clinical data under pre-treatment conditions. For one patient case simulations under post-treatment conditions after geometric repair of CoA by a virtual stenting procedure were compared against pre-treatment results. Numerical stability of the approach was demonstrated by analyzing mesh quality and solver performance under the significantly large deformations of the LV blood pool. Further, computational tractability and compatibility with clinical time scales were investigated by performing strong scaling benchmarks up to 1536 compute cores. The overall cost of the entire workflow for building, fitting and executing EMF simulations was comparable to those reported for image-based kinematic models, suggesting that EMF models show potential of evolving into a viable clinical research tool.
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