Abstract ：

Intracranial aneurysm (IA) is a prevalent cerebrovascular disease associated with high mortality and disability rates upon rupture. The hemodynamics of IA, which are significantly influenced by geometric parameters, directly impact its rupture. This study focuses on investigating the transient flow characteristics in saccular IA models fabricated using a water droplet-based method, specifically examining the influence of neck widths. Particle image velocimetry technique and numerical simulation were employed to investigate the dynamic evolution of flow structures within three IA models. The results reveal that neck width (W) has a substantial effect on flow characteristics in the neck region, subsequently impacting the deep flow inside the sac. Three distinct patterns were observed during flow evolution inside the sac: for W = 2 mm, two vortices occur and then disappear with relatively low average flow velocity; for W = 4 mm, enhanced effects of a high-speed jet result in periodic pulsatile flow velocity distribution while maintaining stable vortex core position; for W = 6 mm, significant changes in flow velocity occur due to size expansion and intensity increase of vortices. These findings demonstrate that neck widths play a complex role in influencing transient flow characteristics within IAs. Overall, this research contributes to further understanding transient flow behaviors in IAs.

Keyword ：

Intracranial aneurysm Neck width Particle image velocimetry Flow pattern Hemodynamics

Cite：

Copy from the list or Export to your reference management。

Abstract ：

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application. © 2024

Keyword ：

Parameter estimation Deep learning Hemodynamics Computational efficiency Numerical models Lumped parameter networks Data reduction Blood

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Endovascular coiling is the predominant method for treating cerebral aneurysms. Extensive reports on selecting coil length, hardness, and material are available. However, the impact of coil diameter on postoperative outcomes remains unclear. This study enrolled six personalized geometric models of intracranial aneurysms: three bifurcation aneurysms and three sidewall aneurysms. Four coil models were constructed by changing the coil diameter. Coil embolization was simulated using the finite element method. Computational fluid dynamics was used to characterize hemodynamics in the aneurysms after embolization. Evaluation parameters included velocity reduction, wall shear stress (WSS), low WSS (LWSS), oscillatory shear index (OSI), relative residence time (RRT), and residual flow volume in the aneurysms. At the peak time (t = 0.17 s), the proportion of LWSS area in bifurcation aneurysms increase with the rise in coil diameter: 0.8D, 71.28 +/- 12.62% versus 1D, 74.97 +/- 19.17% versus 1.2D, 78.88 +/- 18.56% versus 1.4D, 84.00 +/- 11.53% (mean +/- SD). The proportion of high OSI area decreases as the coil diameter increases: 0.8D, 4.41% +/- 2.82% versus 1.0D, 3.78 +/- 3.33% versus 1.2D, 2.28% +/- 1.77% versus 1.4D, 1.58% +/- 1.11% (mean +/- SD). The proportion of high RRT area increases as the coil diameter rises: 0.8D, 3.40% +/- 1.68% versus 1.0D, 7.67 +/- 4.12% versus 1.2D, 9.84% +/- 9.50% versus 1.4D, 22.29% +/- 14.28% (mean +/- SD). Side wall aneurysms do not exhibit the aforementioned trend. Bifurcation aneurysms plugged with a coil of 1.4 times the diameter have the largest RFVs (<10 mm/s) within the group. Aforementioned patterns are not found in sidewall aneurysms. In the treatment of aneurysms with coiling, varying coil diameters can result in different hemodynamic environments within the aneurysm. Larger coil diameters have improved hemodynamic performance for bifurcation aneurysms. However, coil diameter and embolization effectiveness have no significant relationship for sidewall aneurysms.

Keyword ：

intracranial aneurysms finite element method coil embolization hemodynamics computational fluid dynamics

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error < 12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.

Keyword ：

deep learning (DL) carotid artery stenosis (CAS) hemodynamics stroke computational fluid dynamics (CFD)

Cite：

Copy from the list or Export to your reference management。

Abstract ：

This study presents a novel experimental approach to investigate the changes in pulse wave patterns and pulse wave characteristic parameters in patients before, during, and after hemodialysis by analyzing radial artery pulse waves collected at time intervals from end-stage renal disease patients receiving hemodialysis. The results show that the morphology of the pulse wave is characterized by diversity between patients due to different vascular elasticity, but five categories can be classified according to the shape. When individuals of the same waveform Type-Are studied, we found that there were significant differences in some of the characteristic parameters of the pulse wave at different times of the day as the hemodialysis process progressed, a finding that also verifies the existence of hemodynamic changes during hemodialysis.

Keyword ：

Hemodynamics Pulse wave Hemodialysis

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Extracorporeal membrane oxygenation (ECMO) is a life support system used in the treatment of severe respiratory and circulatory failure. High shear stress caused by the high rotational speed of centrifugal blood pumps can cause hemolysis and platelet activation, which are among the major factors leading to the complications of the ECMO system. In this study, a novel blood pump named rotary displacement blood pump (RDBP), which can considerably reduce rotational speed and shear stress while ensuring the normal pressure flow relationship, was proposed. We employed computational fluid dynamics (CFD) analysis to investigate the performance of RDBP under adult ECMO support operating conditions (5 L/min with 350 mmHg). The efficiency and H-Q curves of the RDBP were calculated to evaluate its hydraulic performance, and pressure, flow patterns, and shear stress distribution were analyzed to estimate the hemodynamic characteristics in the pump. In addition, the modified index of hemolysis (MIH) was calculated for the RDBP based on a Eulerian approach. The hydraulic efficiency of the RDBP was 47.28%. The velocity distribution of flow field in the pump was relatively uniform. Most of the liquid (more than 75%) in the pump was exposed to low scale shear stress (<1 Pa), which was close to normal physiological conditions. The gap area was the main distribution location of high scale shear stress. The high wall shear stress (>9 Pa) volume fraction of the RDBP was small and located in the boundary areas between the rotor's edge and the housing. The MIH value of the RDBP was 9.87 +/- 0.93 (mean +/- SD). The RDBP can achieve better hydraulic efficiency and hemodynamic performance at lower rotational speed. The design of this novel pump is expected to provide a new direction for developing a blood pump for ECMO.

Keyword ：

rotary displacement blood pump blood pump CFD ECMO hemodynamics

Cite：

Copy from the list or Export to your reference management。

Abstract ：

The hemodynamics of intracranial aneurysm (IA) comprises complex transient flow patterns that affect its growth and rupture. Owing to the combined effects of geometrical factors and pulsatile flow conditions, the transient flow patterns in the IA are still unclear. The purpose of this work is to reveal the effect of the aspect ratio (AR, sac height/neck width) on the evolution of the internal flow patterns and the hemodynamics of the IA. We proposed an easy method to fabricate three simplified elastic IA models and measured the transient flow characteristics by using particle image velocimetry (PIV). Transient vortex structures in the IA modes during a cardiac cycle were systemically measured and many new flow phenomena were found, including the vortex morphology (size, structure, and core location), a high-speed jet, wall compliance effects, and three flow modes during retrograde flow phase. The results show that the AR of the IA affects the transient flow patterns as well as the wall shear stress (WSS) in complex ways. The results could deepen our understanding of the transient flow behaviors in IA and guide related clinical studies.

Keyword ：

Intracranial aneurysm Flow pattern Hemodynamics Vortex Particle image velocimetry

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Background and objectives: The bicuspid aortic valve (BAV) is a major risk factor for the progression of aortic dilation (AD) because of the induced abnormal blood flow environment in aorta. The differences in the development of AD induced by BAV phenotypes remains unclear. Therefore, the objective of this study was to assess the potential locations of AD induced by different phenotypes of BAV. The different effects of opening orifice area and leaflet orientation on ascending aortic hemodynamics in Type-1 BAV was investigated by means of numerical simulation.Methods: Finite element dynamic analysis was performed on tricuspid aortic valve (TAV) and BAV models to simulate the motion of the leaflets and obtain the geometrical characteristics of AV at peak systole as a reference, which were used for aortic models. Then, four sets of aortic fluid models were designed according to the leaflet fusion types [TAV; BAV (left-right-coronary cusp fusion, LR; right-non-coronary cusp fusion, RN; left-non-coronary cusp fusion, LN)], and the computational fluid dynamics method was applied to compare the hemodynamic differences within the aorta at peak systole. Results: The maximum opening area of BAV was significantly reduced, resulting in alterations in aortic hemodynamics compared with TAV. The velocity streamlines were essentially parallel to the aortic wall in TAV. The average pressure and wall shear stress in aorta tend to be stable. In contrary, the eccentricity of BAV orifice jet resulted in high-velocity flow directed toward the ascending aorta (AA) wall and aortic arch for LR and LN; RN features an asymmetrical velocity distribution toward the outer bend of the middle AA, and eccentric flow tends to impact the distal AA. As the flow angle is associated with distinct flow impingement locations, different degrees of WSS and pressure concentration occur along the aortic wall from the AA to the aortic arch in three BAV types.Conclusions: The BAV morphotype affects the aortic hemodynamics, and the abnormal blood flow associated with BAV may play a role in AD. The different BAV phenotypes determine the direction of blood flow jet and change the expression of dilation. LR is likely to cause dilation of the tubular AA; RN results in dilation of the middle AA to proximal aortic arch; and LN causes an increased incidence of the tubular AA and the proximal aortic arch.(c) 2022 Elsevier B.V. All rights reserved.

Keyword ：

Hemodynamics Numerical simulation Aortic dilation Peak systolic period Bicuspid aortic valve

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Sigmoid sinus wall dehiscence (SSWD) is a common pathophysiology of patients with pulsatile tinnitus (PT). However, the pathological mechanism of SSWD is unclear. This study aimed to investigate the relationship between the position of the SSWD and blood flow pattern of the transverse sinus and sigmoid sinus (TS-SS) junction. The impact of the blood flow was hypothesized to be the pathological mechanism of SSWD. Twenty patients and two healthy volunteers were analyzed retrospectively, and transient computer fluid dynamics was used to verify this hypothesis. A 4D flow magnetic resonance imaging experiment was performed to validate the numerical simulation. The position of high-velocity blood flow impacting the vessel wall (17/20) was consistent with SSWD. In healthy volunteers, the temporal bone was thin where the blood flow impacted the blood vessel wall. The average wall shear stress (20/20) and pressure (18/20) of the SSWD area (peak) were higher than those of sigmoid sinus wall anomalies (the contact area between the vessel wall and the temporal bone at the TS-SS junction). The average wall pressure percentage differences of 16/20, 11/20, and 4/20 patients were more than 5%, 10%, and 20%, respectively. The average wall shear stress percentage differences of 20/20, 18/20, and 16/ 20 patients were more than 5%, 10%, and 20%, respectively. In brief, the blood flow of the TS-SS junction impacted the vessel wall and increased wall pressure, which might be an important pathological mechanism of SSWD. This study could serve as a basis for the diagnosis and SSWD resurfacing surgery of patients with PT induced by SSWD.

Keyword ：

Computational fluid dynamics Transverse sinus and sigmoid sinus junction Pulsatile tinnitus 4D flow MRI Sigmoid sinus wall dehiscence

Cite：

Copy from the list or Export to your reference management。

Abstract ：

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

Keyword ：

cerebral aneurysm deep learning flow-diverting stent hemodynamics porous-medium

Cite：

Copy from the list or Export to your reference management。