Effects of Pulsatile Flow Rate and Shunt Ratio in Bifurcated Distal Arteries on Hemodynamic Characteristics Involved in Two Patient-Specific Internal Carotid Artery Sidewall Aneurysms: A Numerical Study

Using DFT on ultrasound measurements to determine patient-specific blood flow boundary conditions for computational hemodynamics of intracranial aneurysms

Boundary conditions (BCs) is one pivotal factor influencing the accuracy of hemodynamic predictions on intracranial aneurysms (IAs) using computational fluid dynamics (CFD) modeling. Unfortunately, a standard procedure to secure accurate BCs for hemodynamic modeling does not exist. To bridge such a knowledge gap, two representative patient-specific IA models (Case-I and Case-II) were reconstructed and their blood flow velocity waveforms in the internal carotid artery (ICA) were measured by ultrasonic techniques and modeled by discrete Fourier transform (DFT). Then, numerical investigations were conducted to explore the appropriate number of samples (N) for DFT modeling to secure the accurate BC by comparing a series of hemodynamic parameters using in-vitro validated CFD modeling. Subsequently, a comprehensive comparison in hemodynamic characteristics under patient-specific BCs and a generalized BC based on a one-dimensional (1D) model was conducted to reinforce the understanding that a patient-specific BC is pivotal for accurate hemodynamic risk evaluations on IA pathophysiology. In addition, the influence of the variance of heart rate/cardiac pulsatile period on hemodynamic characteristics in IA models was studied preliminarily. The results showed that N ≥ 16 for DFT model is a decent choice to secure the proper BC profile to calculate time-averaged hemodynamic parameters, while more data points such as N ≥ 36 can ensure the accuracy of instantaneous hemodynamic predictions. In addition, results revealed the generalized BC could overestimate or underestimate the hemodynamic risks on IAs significantly; thus, patient-specific BCs are highly recommended for hemodynamic modeling for IA risk evaluation. Furthermore, this study discovered the variance of heart rate has rare influences on hemodynamic characteristics in both instantaneous and time-averaged parameters under the assumption of an identical blood flow rate.

Pathophysiology of intracranial aneurysms in monozygotic twins: A rare case study from hemodynamic perspectives

Hemodynamic mechanisms of the formation and growth of intracranial aneurysms (IA) in monozygotic twins (MTs) are still under-reported. To partially fill such knowledge gap, this study employed an experimentally validated numerical model to compare hemodynamics in 3 anatomical and 5 ablation study neurovascular models from a rare pair of MTs in terms of 7 critical hemodynamic parameters. Numerical results showed significant differences in hemodynamics between the MTs, although they share the same genes, indicating that genetic mutation and environmental factors might affect neurovascular morphologies and cause hemodynamic changes. After virtual removals of IAs in the ablation study, the locations where the aneurysmal sac/bleb generated in bifurcated anterior cerebral arteries (ACAs) register a locally high instantaneous wall shear stress (IWSS) of 52.9 and 70.1 Pa at the systolic peak in twin A and twin B, respectively. Same scenario can be observed in the distribution of instantaneous wall shear stress gradient (IWSSG), with 571.1 Pa/mm for twin A and 301.3 Pa/mm for twin B due to aggressive blood impingements, leading to IA generation. The fenestrated complex approaching ACA bifurcations in twin A may assist IA growth and rupture, via. Causing abnormal IWSS of 116.3 Pa, IWSSG of 832.5 Pa/mm, and oscillatory shear index (OSI) of 0.49. The bleb in twin B has high risks of progression and possible rupture as the IA suffers relatively low IWSS and high OSI. Additionally, IA generation can change blood flow rates in each connected artery, then affecting blood supplies to associated tissues and organs.

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Introduction: Direct quantification of hemodynamic factors applied to a cerebral aneurysm (CA) remains inaccessible due to the lack of technologies to measure the flow field within an aneurysm precisely. This study aimed to develop an in vitro validated 3D in silico patient-specific internal carotid artery sidewall aneurysm (ICASA) model which can be used to investigate hemodynamic factors on the CA pathophysiology. Methods: The validated ICASA model was developed by quantifying and comparing the flow field using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Specifically, the flow field characteristics, i.e., blood flowrates, normalized velocity profiles, flow streamlines, and vortex locations, have been compared at representative time instants in a cardiac pulsatile period in two designated regions of the ICASA model, respectively. One region is in the internal carotid artery (ICA) inlet close to the aneurysm sac, the other is across the middle of the aneurysmal sac. Results and Discussion: The results indicated that the developed computational fluid dynamics model presents good agreements with the results from the parallel particle image velocimetry and flowrate measurements, with relative differences smaller than 0.33% in volumetric flow rate in the ICA and relative errors smaller than 9.52% in averaged velocities in the complex aneurysmal sac. However, small differences between CFD and PIV in the near wall regions were observed due to the factors of slight differences in the 3D printed model, light reflection and refraction near arterial walls, and flow waveform uncertainties. The validated model not only can be further employed to investigate hemodynamic factors on the cerebral aneurysm pathophysiology statistically, but also provides a typical model and guidance for other professionals to evaluate the hemodynamic effects on cerebral aneurysms.

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10-3 and 5 × 10-7 m/s, respectively, with a standard deviation less than 1 × 10-6 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.

Evaluation of hemodynamic effects of different inferior vena cava filter heads using computational fluid dynamics

Inferior vena cava (IVC) filters are used to prevent pulmonary embolism in patients with deep vein thrombosis for whom anticoagulation is unresponsive. The head is a necessary structure for an Inferior vena cava filter (IVCF) in clinic use. At present, there are various head configurations for IVCFs. However, the effect of head pattern on the hemodynamics of IVCF is still a matter of unclear. In this study, computational fluid dynamics is used to simulate non-Newtonian blood flows around four IVCFs with different heads inside an IVC model, in which the Denali filter with a solid and hooked head is employed as a prototype, and three virtual variants are reconstructed either with a no-hook head or with a through-hole head for comparison. The simulation results show that the through-hole head can effectively avoid the recirculation region and weaken the blood flow stasis closely downstream the IVCF head. The shape change of the filter head has no significant effect on the blood flow acceleration inside the IVCF cone as well as little influence on the wall shear stress (WSS) distribution on the filter wire surface and IVC wall. The structure pattern of filter head greatly affects the flow resistance of its own. However, the flow drag of filter head only occupies a small proportion of the total resistance of IVCF. Therefore, to reduce the flow resistance of an IVCF should optimize its whole structure.

Hemodynamic characteristics in a cerebral aneurysm model using non-Newtonian blood analogues

This study aims to develop an experimentally validated computational fluid dynamics (CFD) model to estimate hemodynamic characteristics in cerebral aneurysms (CAs) using non-Newtonian blood analogues. Blood viscosities varying with shear rates were measured under four temperatures first, which serves as the reference for the generation of blood analogues. Using the blood analogue, particle image velocimetry (PIV) measurements were conducted to quantify flow characteristics in a CA model. Then, using the identical blood properties in the experiment, CFD simulations were executed to quantify the flow patterns, which were used to compare with the PIV counterpart. Additionally, hemodynamic characteristics in the simplified Newtonian and non-Newtonian models were quantified and compared using the experimentally validated CFD model. Results showed the proposed non-Newtonian viscosity model can predict blood shear-thinning properties accurately under varying temperatures and shear rates. Another developed viscosity model based on the blood analogue can well represent blood rheological properties. The comparisons in flow characteristics show good agreements between PIV and CFD, demonstrating the developed CFD model is qualified to investigate hemodynamic factors within CAs. Furthermore, results show the differences of absolute values were insignificant between Newtonian and non-Newtonian fluids in the distributions of wall shear stress (WSS) and oscillatory shear index (OSI) on arterial walls. However, not only does the simplified Newtonian model underestimate WSS and OSI in most regions of the aneurysmal sac, but it also makes mistakes in identifying the high OSI regions on the sac surface, which may mislead the hemodynamic assessment on the pathophysiology of CAs.