Narrow Results

The precise evaluations of intracranial aneurysms (IAs) are highly prominent for the treatment and control of aneurysm rupture. Computational fluid dynamic (CFD) simulations based on angiography image is a reliable tool for the recognition of high-risk region and aneurysm status in recent years. In our study, the CFD is used to investigate the impacts of blood hematocrit and coiling techniques on the risk of aneurysm rupture. To do this, wall shear stress (WSS), oscillatory shear index (OSI) and pressure distribution on the wall of an aneurysm are comprehensively evaluated in various coding porosities and blood viscosities. One-way Fluid Solid Interaction (FSI) technique is applied to investigate the non-Newtonian, pulsatile blood stream inside the sac of the aneurysm. Impacts of two coiling porosities and blood hematocrits of 0.3 and 0.5 on blood features inside the sac are also analyzed. The influence of the blood mass flow rate in four different time instants of blood cycles on the size of the high-risk region on the aneurysm wall is demonstrated. Our results show that more than 40% reduction is noticed when the hematocrit (HCT) of blood is reduced from 0.5 to 0.3 in different time instants. Our findings also reveal that decreasing the porosity from 0.96 to 0.74 in the peak systolic stage results in a 28% reduction in the maximum OSI at specific HCT = 0.4.

We introduce a new mathematical model of aneurysm of the circle of Willis. It is an ordinary differential equation of second order that regulates the velocity of blood flow inside the aneurysm. By using some recent methods of nonlinear analysis, we prove the existence of solutions with some qualitative properties that give information on the causes of rupture of the aneurysm.

Aneurysms of the hand are uncommon lesions. The most common location is the ulnar artery. We present a case of a young female who consulted us for a hand mass with a history of trauma to the hand. Pre-operative arteriogram showed a superficial palmar arch aneurysm. The mass was excised and the arch was reconstructed using a reversed Y-shaped vein graft. Fourteen months after surgery, there was no recurrence of the aneurysm and the patient only reported occasional cold intolerance.

The aim of this study was to model the blood flow and predict related hemodynamics characteristics in healthy superior mesenteric artery (SMA) and saccular aneurysm cases. A fluid–structure interaction (FSI) method was performed, using an arbitrary Langrangian–Eulerian mesh. The computational mesh was generated using anatomical data from available human computed tomography (CT)-images. Combining constitution and momentum equations, projection method, the discretized resultant equation were numerically solved for velocity, pressure, shear stress and vortices for healthy/aneurysmal artery. The results including velocity contours, pressure contours, shear rate values, and vortices were obtained and analyzed for three main steps including peak systole, diastole, and end of cardiac cycle. Profiles show the varying velocity and pressure for a pulsatile flow input before and after aneurysms. They also show the formation of single or multiple vortices at aneurysmal area and decrease of wall shear stress with aneurysm enlargement. Furthermore, shear rate values at the neck of aneurysms exceed throughout the entire cardiac cycle. The outcome of the computational analysis is then compared to information available on pressure, vortices and wall shear stress from some clinical findings.

The aneurysm of iliac artery is a rare entity and there are few computational models that have studied the disease. In this study, we have presented the flow patterns in the aneurysmal artery using Fluid–structure interaction method. The blood was assumed Newotonian, pulsatile, laminar, incompressible, and homogenous. The geometry of the model was made based on CT images of clinical cases. Using the computational method, we have obtained the velocity and pressure contours, shear rates and vortices for the healthy and aneurysmal artery. The results show that a pressure maximum was found at the midpoint of the dilation. The vortices are formed in the aneurysmal area²⁶ and shear rates do not change much. However, the rate increased in the neck of aneurysms. Furthermore, the aneurysm with bigger dilation tend to rupture due to more shear rates in the neck and the velocity at peak systole decreases in the aneurysmal area due to increase of the artery diameter. We have compared our results with some available relevant clinical data in discussion section.

“Image-based” computational fluid dynamics (CFD) simulations provide insights into each patient’s hemodynamic environment. However, the current standard procedures for creating CFD models start with manual segmentation and are time-consuming, hindering the clinical translation of image-based CFD simulations. This feasibility study adopts deep-learning-based image segmentation [hereafter referred to as Artificial Intelligence (AI) segmentation] to replace manual segmentation to accelerate the CFD model creation. Two published convolutional neural network-based AI methods (MIScnn and DeepMedic) were selected to perform CFD model extraction from three-dimensional (3D) rotational angiography data containing intracranial aneurysms. In this study, aneurysm morphological and hemodynamic results using the models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICCs), Bland–Altman plots, and Pearson’s correlation coefficients (PCCs) were combined to assess how well the AI-generated CFD models performed. We found that almost perfect agreement was obtained between the human and AI results for all 11 morphological parameters and five out of eight hemodynamic parameters, while a moderate agreement was obtained from the remaining three hemodynamic parameters. Given this level of agreement, using AI segmentation to create CFD models is feasible, given more developments.

Using the data obtained from the computational fluid dynamics simulations, a back-propagation neural network model was developed to predict the velocity magnitudes and the instantaneous wall shear stresses in two patient-specific aneurysms. The models were also used to determine the effect of the blood composition on the rapture risk of the aneurysms. Based on the possible combination, five back propagation models were developed. The architecture of five models is determined based on number of neurons in the hidden layer. All the models in each algorithm were trained and tested. The accuracy of the developed models was evaluated through statistical analysis of the network output in terms of mean absolute error, root mean squared error, mean squared error, and error deviation. According to the results obtained, all BPA effectively predicted velocity magnitude and instantaneous wall shear stress. Model 1 was, however, less accurate when compared to the other five models, as it had one neuron in its hidden layer. The analysis confirms that the neuron number in the hidden layer play a definitive role in predicting the respective outputs. The performance assessment all of the back-propagation models revealed that the error incurred was acceptable. The algorithms’ training and testing in this study were satisfactory, since the network output was in reasonably good conformity with the target computational fluid dynamics result.

Computational fluid dynamics analyses were performed on three models which have a giant aneurysm with or without a stenosis. The first is a model with an aneurysm (no stenosis and no stent), the second is a model with a preaneurysm stenosis, and the third is a model with an aneurysm implanted with a stent. The increase in pressure in aneurismal sac caused by a 50% stenosis is about 10.3mmHg at peak systole (comparison between the second model and the first model). It must pay attention to the increase of the pressure for the patient which has an aneurysm accompanied by a stenosis when making the treatment plan. Otherwise, it may cause the aneurysm rupture.

The superficial temporal artery (STA) is a structure that is particularly vulnerable to injury, given its prominent location in the head and neck region. Pseudoaneurysms of this artery may be encountered during the management of maxillofacial trauma. This article presents a review of the relevant literature on this topic. One of the common surgical interventions includes ligation of the entire feeding artery, which compromises the corresponding blood supply. Preservation of the main trunk of the STA can be achieved in certain cases; one such case is detailed in this report.