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

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.

A bio-fabricated tesla valves and ultrasound waves-powered blood plasma viscometer

Introduction: There is clinical evidence that the fresh blood viscosity is an important indicator in the development of vascular disorder and coagulation. However, existing clinical viscosity measurement techniques lack the ability to measure blood viscosity and replicate the in-vivo hemodynamics simultaneously. Methods: Here, we fabricate a novel digital device, called Tesla valves and ultrasound waves-powered blood plasma viscometer (TUBPV) which shows capacities in both viscosity measurement and coagulation monitoring. Results: Based on the Hagen-Poiseuille equation, viscosity analysis can be faithfully performed by a video microscopy. Tesla-like channel ensured unidirectional liquid motion with stable pressure driven that was triggered by the interaction of Tesla valve structure and ultrasound waves. In few seconds the TUBPV can generate an accurate viscosity profile on clinic fresh blood samples from the flow time evaluation. Besides, Tesla-inspired microchannels can be used in the real-time coagulation monitoring. Discussion: These results indicate that the TUBVP can serve as a point-of-care device in the ICU to evaluate the blood's viscosity and the anticoagulation treatment.

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.

Morphology and Hemodynamics of Cerebral Arteries and Aneurysms in a Rare Pair of Monozygotic Twins

In this preliminary study, the underlying pathophysiology mechanisms of cerebral aneurysms (CAs) in monozygotic twins (MTs) were investigated via a rare pair of MTs (twin A and twin B) involving four reconstructed arterial models using preclinical information. First, dimensions and configurated outlines of three-perspective geometries were compared. Adopting an in-vitro validated numerical CA model, hemodynamic characteristics were investigated in the MTs, respectively. Despite expected genetic similarities, morphological comparisons show that configurations of cerebral arteries exhibit significant differences between the twins. The ICA size of twin A is larger than that in twin B (2.23~25.86%), varying with specific locations, attributing to variations during embryological developments and environmental influences. Numerical modeling indicates the MTs have some hemodynamic similarities such as pressure distributions (~13,400 Pa) and their oscillatory shear index (OSI) (0~0.49), but present significant differences in local regions. Specifically, the difference in blood flow rate in the MTs is from 16% to 221%, varying with specifically compared arteries. The maximum time-averaged wall shear stress (53.6 Pa vs. 37.8 Pa) and different local OSI distributions were also observed between the MTs. The findings revealed that morphological variations in MTs could be generated by embryological and environmental factors, further influencing hemodynamic characteristics on CA pathophysiology.

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.