Modelling the circle of Willis to assess the effects of anatomical variations and occlusions on cerebral flows

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow

BACKGROUND AND OBJECTIVE

Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries.

METHODS

Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics.

RESULTS

The accuracy was validated in silico with different arterial networks, where PIGNNs achieved a coefficient of determination (R2) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with in vivo data, the prediction reached R2 values greater than 0.80, demonstrating the method's effectiveness in predicting flow and lumen dynamics using minimal data.

CONCLUSIONS

This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.

Enhancing cerebral arteriovenous malformation analysis: Development and application of patient-specific lumped parameter models based on 3D imaging data

OBJECTIVES

Cerebral arteriovenous malformations (AVMs) present complex neurovascular challenges, characterized by direct arteriovenous connections that disrupt normal brain blood flow dynamics. Traditional lumped parameter models (LPMs) offer a simplified angioarchitectural representation of AVMs, yet often fail to capture the intricate structure within the AVM nidus. This research aims at refining our understanding of AVM hemodynamics through the development of patient-specific LPMs utilizing three-dimensional (3D) medical imaging data for enhanced structural fidelity.

METHODS

This study commenced with the meticulous delineation of AVM vascular architecture using threshold segmentation and skeletonization techniques. The AVM nidus's core structure was outlined, facilitating the extraction of vessel connections and the formation of a detailed fistulous vascular tree model. Sampling points, spatially distributed and derived from the pixel intensity in imaging data, guided the construction of a complex plexiform tree within the nidus by generating smaller Y-shaped vascular formations. This model was then integrated with an electrical analog model to enable precise numerical simulations of cerebral hemodynamics with AVMs.

RESULTS

The study successfully generated two distinct patient-specific AVM networks, mirroring the unique structural and morphological characteristics of the AVMs as captured in medical imaging. The models effectively represented the intricate fistulous and plexiform vessel structures within the nidus. Numerical analysis of these models revealed that AVMs induce a blood shunt effect, thereby diminishing blood perfusion to adjacent brain tissues.

CONCLUSION

This investigation enhances the theoretical framework for AVM research by constructing patient-specific LPMs that accurately reflect the true vascular structures of AVMs. These models offer profound insights into the hemodynamic behaviors of AVMs, including their impact on cerebral circulation and the blood steal phenomenon. Further incorporation of clinical data into these models holds the promise of deepening the theoretical comprehension of AVMs and fostering advancements in the diagnosis and treatment of AVMs.

Cognitive impairment in asymptomatic carotid artery stenosis is associated with abnormal segments in the Circle of Willis

OBJECTIVE

Our group has previously demonstrated that patients with asymptomatic carotid artery stenosis (ACAS) demonstrate cognitive impairment. One proposed mechanism for cognitive impairment in patients with ACAS is cerebral hypoperfusion due to flow-restriction. We tested whether the combination of a high-grade carotid stenosis and inadequate cross-collateralization in the Circle of Willis (CoW) resulted in worsened cognitive impairment.

METHODS

Twenty-four patients with high-grade (≥70% diameter-reducing) ACAS underwent carotid duplex ultrasound, cognitive assessment, and 3D time-of-flight magnetic resonance angiography. The cognitive battery consisted of nine neuropsychological tests assessing four cognitive domains: learning and recall, attention and working memory, motor and processing speed, and executive function. Raw cognitive scores were converted into standardized T-scores. A structured interpretation of the magnetic resonance angiography images was performed with each segment of the CoW categorized as being either normal or abnormal. Abnormal segments of the CoW were defined as segments characterized as narrowed or occluded due to congenital aplasia or hypoplasia, or acquired atherosclerotic stenosis or occlusion. Linear regression was used to estimate the association between the number of abnormal segments in the CoW, and individual cognitive domain scores. Significance was set to P < .05.

RESULTS

The mean age of the patients was 66.1 ± 9.6 years, and 79.2% (n = 19) were male. A significant negative association was found between the number of abnormal segments in the CoW and cognitive scores in the learning and recall (β = -6.5; P = .01), and attention and working memory (β = -7.0; P = .02) domains. There was a trend suggesting a negative association in the motor and processing speed (β = -2.4; P = .35) and executive function (β = -4.5; P = .06) domains that did not reach significance.

CONCLUSIONS

In patients with high-grade ACAS, the concomitant presence of increasing occlusive disease in the CoW correlates with worse cognitive function. This association was significant in the learning and recall and attention and working memory domains. Although motor and processing speed and executive function also declined numerically with increasing abnormal segments in the CoW, the relationship was not significant. Since flow restriction at a carotid stenosis compounded by inadequate collateral compensation across a diseased CoW worsens cerebral perfusion, our findings support the hypothesis that cerebral hypoperfusion underlies the observed cognitive impairment in patients with ACAS.

A Computational Pipeline to Investigate Longitudinal Blood Flow Changes in the Circle of Willis of Patients with Stable and Growing Aneurysms

Changes in cerebral blood flow are often associated with the initiation and development of different life-threatening medical conditions including aneurysm rupture and ischemic stroke. Nevertheless, it is not fully clear how haemodynamic changes in time across the Circle of Willis (CoW) are related with intracranial aneurysm (IA) growth. In this work, we introduced a novel reduced-order modelling strategy for the systematic quantification of longitudinal blood flow changes across the whole CoW in patients with stable and unstable/growing aneurysm. Magnetic Resonance Angiography (MRA) images were converted into one-dimensional (1-D) vessel networks through a semi-automated procedure, with a level of geometric reconstruction accuracy controlled by user-dependent parameters. The proposed pipeline was used to systematically analyse longitudinal haemodynamic changes in seven different clinical cases. Our preliminary simulation results indicate that growing aneurysms are not necessarily associated with significant changes in mean flow over time. A concise sensitivity analysis also shed light on which modelling aspects need to be further characterized to have reliable patient-specific predictions. This study poses the basis for investigating how time-dependent changes in the vasculature affect the haemodynamics across the whole CoW in patients with stable and growing aneurysms.

Investigation of paraclinoid aneurysm formation by comparing the combined influence of hemodynamic parameters between aneurysmal and non-aneurysmal arteries

Numerous studies have evaluated the effects of hemodynamic parameters on aneurysm formation. However, the reasons why aneurysms do not initiate in intracranial arteries are still unclear. This study aimed to investigate the influence of hemodynamic parameters, wall shear stress (WSS) and strain, on aneurysm formation by comparing between aneurysmal and non-aneurysmal arteries. Fifty-eight patients with paraclinoid aneurysms on one side were enrolled. Based on magnetic resonance angiography, each patient's left and right internal carotid arteries (ICAs) were reconstructed. For a patient having an aneurysm on one side, the ICA with the paraclinoid aneurysm was defined as the aneurysmal artery after eliminating the aneurysm, whereas the opposite ICA without aneurysm was defined as the non-aneurysmal artery. Computational fluid dynamics and fluid-structure interaction analyses were then performed for both aneurysmal and non-aneurysmal arteries. Finally, the relationship between high hemodynamic parameters and aneurysm location was investigated. For aneurysmal arteries, high WSS and strain locations were well-matched with the aneurysm formation site. Also, considerable correlations between high WSS and strain locations were observed. However, there was no significant relationship between high hemodynamic parameters and aneurysm formation for non-aneurysmal arteries. The findings are helpful for understanding aneurysm formation mechanism and encouraging further relevant research.

Carotid occlusion of a giant intracavernous aneurysm on a single functional internal carotid artery

The prevalence of intracranial aneurysms (IA) is higher in patients with stenosis of the internal carotid artery (ICA), the intracavernous internal carotid aneurysm is an intracranial aneurysm causing major functional and vital complications. We report the case of a 26-year-old man who consulted for a reduction in visual acuity, converging strabismus and ptosis of the right eye evolving for 7 months before his consultation, the various neuro-radiological examinations made it possible to identify diagnose a giant aneurysm of the right intracavernous internal carotid artery associated with severe stenosis of the contralateral internal carotid artery, hence the performance of a therapeutic arteriography consisting of an occlusion of this aneurysm. In summary, we describe successful management of a giant aneurysm of the intracavernous portion of a single functional internal carotid artery, while preserving optimal cerebral vascularization.

Static and dynamic analysis of cerebral blood flow in fifty-six large arterial vessel networks

Objective.The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset.Approach.We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort.Main results.Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency.Significance.These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.

An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

Human cerebral blood supply via circulus arteriosus cerebri: A scoping review on its variations and clinical implications

Background

Circulus arteriosus cerebri (CAC), responsible for supplying blood to the brain, presents anatomical variations that have been associated with both haemorrhagic and ischemic strokes. Therefore, it is crucial to conduct comprehensive investigations and comparisons of the diverse variant components of the CAC, published in various journals, and analyze them to identify individuals at risk of cerebrovascular pathologies, thereby ensuring enhanced and timely treatment.

Methods

A scoping review according to the five-stage protocol by Arksey and O'Malley was performed between February and June 2023. Seven hundred and seventy-seven records were initially identified, and a total of 51 studies were finally included.

Results

This scoping review focuses on the anatomical variations of the CAC and their clinical implications on cerebrovascular disease and includes more original articles than review s. Consistent with previous findings, most of the records included small populations or samples, while only three records reported larger populations. Surprisingly, the populations enclosed in the included records consisted of autopsied cadaveric specimens more than living subjects. Finally, the qualitative analysis highlighted three main themes concerning the relationship between the normal CAC morphology and the cerebrovascular disease onset as well as the variant CAC morphology and its main features that might be also involved in these diseases. Finally, techniques that can be used to measure CAC have also been assessed.

Conclusion

Variations in the CAC, more common in the posterior part, with genetic and environmental factors influencing these variations impact cerebrovascular disorders. Understanding variants components of CAC can aid in improving brain surgeries and post-stroke care.

Establishing the distribution of cerebrovascular resistance using computational fluid dynamics and 4D flow MRI

Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data with computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR (tCVR). CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg s/ml for the MCA, ACA, and PCA territories. We found no differences in tCVR between patients, 9.3 ± 1.9 mmHg s/ml, and controls, 9.3 ± 2.0 mmHg s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.

Identifying hemodynamic factors associated with the rupture of anterior communicating artery aneurysms based on global modeling of blood flow in the cerebral artery network

Anterior communicating artery (ACoA) aneurysms are more prone to rupture compared to aneurysms present in other cerebral arteries. We hypothesize that systemic blood flow in the cerebral artery network plays an important role in shaping intra-aneurysmal hemodynamic environment thereby affecting the rupture risk of ACoA aneurysms. The majority of existing numerical studies in this field employed local modeling methods where the physical boundaries of a model are confined to the aneurysm region, which, though having the benefit of reducing computational cost, may compromise the physiological fidelity of numerical results due to insufficient account of systemic cerebral arterial hemodynamics. In the present study, we firstly carried out numerical experiments to address the difference between the outcomes of local and global modeling methods, demonstrating that local modeling confined to the aneurysm region results in inaccurate predictions of hemodynamic parameters compared with global modeling of the ACoA aneurysm as part of the cerebral artery network. Motivated by this finding, we built global hemodynamic models for 40 ACoA aneurysms (including 20 ruptured and 20 unruptured ones) based on medical image data. Statistical analysis of the computed hemodynamic data revealed that maximum wall shear stress (WSS), minimum WSS divergence, and maximum WSS gradient differed significantly between the ruptured and unruptured ACoA aneurysms. Optimal threshold values of high/low WSS metrics were determined through a series of statistical tests. In the meantime, some morphological parameters of aneurysms, such as large nonsphericity index, aspect ratio, and bottleneck factor, were found to be associated closely with aneurysm rupture. Furthermore, multivariate logistic regression analyses were performed to derive models combining hemodynamic and morphological parameters for discriminating the rupture status of aneurysms. The capability of the models in rupture status discrimination was high, with the area under the receiver operating characteristic curve reaching up to 0.9. The findings of the study suggest that global modeling of the cerebral artery network is essential for reliable quantification of hemodynamics in ACoA aneurysms, disturbed WSS and irregular aneurysm morphology are associated closely with aneurysm rupture, and multivariate models integrating hemodynamic and morphological parameters have high potential for assessing the rupture risk of ACoA aneurysms.

Numerical simulation and fast method for the 0D-1D multi-scale coupled model and its application in ischemic brain tissue blood flow problems

As living standards rise, more and more people are paying attention to their own health, especially issues such as cerebral thrombosis, cerebral infarction, and other cerebral blood flow problems. An accurate simulation of blood flow within cerebral vessels has emerged as a crucial area of research. In this study, we focus on microcirculatory blood flow in ischemic brain tissue and employ a 0D-1D geometric multi-scale coupled model to characterize this process. Given the intricate nature of human cerebral vessels, we apply a numerical method combining the finite element method and the third-order Runge-Kutta method to resolve the coupled model. To enhance computational efficiency, we introduce a fast method based on the reduced-order extrapolation algorithm. Our numerical example underscores the stability of the method and convergence accuracy to O h 3 + τ 3 , while significantly improving the accuracy and efficiency of blood flow simulation, making the mechanism analysis more accurate. Additionally, we present examples detailing variations and distribution of intracranial pressure and blood flow in ischemic brain tissue throughout a cardiac cycle. Both reduced vascular compliance and vascular stenosis can have adverse effects on intracranial cerebral pressure and blood flow, leading to insufficient local oxygen supply and negative effects on brain function. Meanwhile, there will also be corresponding changes in volume flow and pulsatile blood pressure.

Hemodynamic characteristics and mechanism for intracranial aneurysms initiation with the circle of Willis anomaly

Clinically, circle of Willis (CoW) is prone to anomaly and is also the predominant incidence site of intracranial aneurysms (IAs). This study aims to investigate the hemodynamic characteristics of CoW anomaly, and ascertain the mechanism of IAs initiation from the perspective of hemodynamics. Thus, the flow of IAs and pre-IAs were analyzed for one type of cerebral artery anomaly, that is, anterior cerebral artery A1 segment (ACA-A1) unilateral absence. Three patient geometrical models with IAs were selected from Emory University Open Source Data Center. IAs were virtually removed from the geometrical models to simulate the pre-IAs geometry. For calculation methods, a one-dimensional (1-D) solver and a three-dimensional (3-D) solver were combined to obtain the hemodynamic characteristics. The numerical simulation revealed that the average flow of Anterior Communicating Artery (ACoA) is almost zero when CoW is complete. In contrast, ACoA flow increases significantly in the case of ACA-A1 unilateral absence. For per-IAs geometry, the jet flow is found at the bifurcation between contralateral ACA-A1 and ACoA, which exhibits characteristics of high Wall Shear Stress (WSS) and high wall pressure in the impact region. It triggers the initiation of IAs from the perspective of hemodynamics. The vascular anomaly that leads to jet flow should be considered as a risk factor for IAs initiation.

Towards the mechanical characterisation of unruptured intracranial aneurysms: Numerical modelling of interactions between a deformation device and the aneurysm wall

Intracranial aneurysm is a critical pathology related to the arterial wall deterioration. This work is an essential aspect of a large scale project aimed at providing clinicians with a non-invasive patient-specific decision support tool regarding the rupture risk assessment. A machine learning algorithm links the aneurysm shape observed and a database of UIA clinical images associated with in vivo wall mechanical properties and rupture characterisation. The database constitution is derived from a device prototype coupled with medical imaging. It provides the mechanical characterisation of the aneurysm from the wall deformation obtained by inverse analysis based on the variation of luminal volume. Before performing in vivo tests of the device on small animals, a numerical model was built to quantify the device's impact on the aneurysm wall under natural blood flow conditions. As the clinician will never be able to precisely situate the device, several locations were considered. In preparation for the inverse analysis procedure, artery material laws of increasing complexity were studied (linear elastic, hyper elastic Fung-like). Considering all the device locations and material laws, the device induced relative displacements to the Systole peak (worst case scenario with the highest mechanical stimulus linked to the blood flow) ranging from 375 μm to 1.28 mm. The variation of luminal volume associated with the displacements was between 0.95 % and 4.3 % compared to the initial Systole volume of the aneurysm. Significant increase of the relative displacements and volume variations were found with the study of different cardiac cycle moments between the blood flow alone and the device application. For forthcoming animal model studies, Spectral Photon CT Counting, with a minimum spatial resolution of 250 μm, was selected as the clinical imaging technique. Based on this preliminary study, the displacements and associated volume variations (baseline for inverse analyse), should be observable and exploitable.

Central arterial pressure estimation based on two peripheral pressure measurements using one-dimensional blood flow simulation

Aortic pressure can be estimated using one-dimensional arterial flow simulations. This study demonstrates that two peripheral pressure measurements can be used to acquire the central pressure curve through the patient-specific optimization of a set of system parameters. Radial and carotid pressure measurements and parameter optimization were performed in the case of 62 patients. The two calculated aortic curves were in good agreement, Systolic and Mean Blood Pressures differed on average by 0.5 and -0.5 mmHg, respectively. Good agreement was achieved with the transfer function method as well. The effect of carotid clamping is demonstrated using one resulting patient-specific arterial network.

Non-Invasive Tools in Perioperative Stroke Risk Assessment for Asymptomatic Carotid Artery Stenosis with a Focus on the Circle of Willis

This review aims to explore advancements in perioperative ischemic stroke risk estimation for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis (CoW) morphology based on the CTA or MR diagnostic imaging in the current preoperative diagnostic algorithm. Functional transcranial Doppler (fTCD), near-infrared spectroscopy (NIRS), and optical coherence tomography angiography (OCTA) are discussed in the context of evaluating cerebrovascular reserve capacity and collateral vascular systems, particularly the CoW. These non-invasive diagnostic tools provide additional valuable insights into the cerebral perfusion status. They support biomedical modeling as the gold standard for the prediction of the potential impact of carotid artery stenosis on the hemodynamic changes of cerebral perfusion. Intraoperative risk assessment strategies, including selective shunting, are explored with a focus on CoW variations and their implications for perioperative ischemic stroke and cognitive function decline. By synthesizing these insights, this review underscores the potential of non-invasive diagnostic methods to support clinical decision making and improve asymptomatic patient outcomes by reducing the risk of perioperative ischemic neurological events and preventing further cognitive decline.

In vivo mechanical characterization of arterial wall using an inverse analysis procedure: application on an animal model of intracranial aneurysm

Intracranial aneurysm is a pathology related to the deterioration of the arterial wall. This work is an essential part of a large-scale project aimed at providing clinicians with a non-invasive patient-specific decision support tool to facilitate the rupture risk assessment. It will lean on the link between the aneurysm shape clinically observed and a database derived from the in vivo mechanical characterization of aneurysms. To supply this database, a deformation device prototype of the arterial wall was developed. Its use coupled with medical imaging (spectral photon-counting computed tomography providing a spatial resolution down to 250 μm) is used to determine the in vivo mechanical properties of the wall based on the inverse analysis of the quantification of the wall deformation observed experimentally. This study presents the in vivo application of this original procedure to an animal model of aneurysm. The mechanical properties of the aneurysm wall identified were consistent with the literature, and the errors between the numerical and experimental results were less than 10%. Based on these parameters, this study allows the assessment of the aneurysm stress state for a known solicitation and points towards the definition of a rupture criterion.

Modeling cerebrovascular responses to assess the impact of the collateral circulation following middle cerebral artery occlusion

OBJECTIVE

An improved understanding of the role of the leptomeningeal collateral circulation in blood flow compensation following middle cerebral artery (MCA) occlusion can contribute to more effective treatment development for ischemic stroke. The present study introduces a model of the cerebral circulation to predict cerebral blood flow and tissue oxygenation following MCA occlusion.

METHODS

The model incorporates flow regulation mechanisms based on changes in pressure, shear stress, and metabolic demand. Oxygen saturation in cerebral vessels and tissue is calculated using a Krogh cylinder model. The model is used to assess the effects of changes in oxygen demand and arterial pressure on cerebral blood flow and oxygenation after MCA occlusion.

RESULTS

An increase from five to 11 leptomeningeal collateral vessels was shown to increase the oxygen saturation in the region distal to the occlusion by nearly 100%. Post-occlusion, the model also predicted a loss of autoregulation and a decrease in flow to the ischemic territory as oxygen demand was increased; these results were consistent with data from experiments that induced cerebral ischemia.

CONCLUSIONS

This study highlights the importance of leptomeningeal collaterals following MCA occlusion and reinforces the idea that lower oxygen demand and higher arterial pressure improve conditions of flow and oxygenation.

Development of a 3D Vascular Network Visualization Platform for One-Dimensional Hemodynamic Simulation

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D-0D simulations. This platform covers the entire workflow, from modeling to dynamic 3D visualization of simulation results. Two case studies on, respectively, carotid stenosis and arterial remodeling demonstrate its utility in blood flow simulation applications.

Anatomical variations in the cerebral arterial circle of the Saimaa (Pusa hispida saimensis) and Baltic ringed seals (Pusa hispida botnica)

The intracranial arterial vascularization of the Saimaa ringed seals (Pusa hispida saimensis; Nordquist, 1899) and Baltic ringed seals (Pusa hispida botnica; Gmelin, 1788) disclosed patterns of anatomical architecture comparable to that of other pinniped species. Arterial silicone casts on skull scaffolds, and magnetic resonance imaging (MRI) showed that the besides joining the caudal communicating arteries upon entering the cerebral arterial circle, the bilateral internal carotid arteries bifurcated as laterally oriented rostral choroidal arteries and rostral cerebral arteries. The latter arteries almost immediately gave off the laterally oriented middle cerebral arteries. Numerous individual variations were evident in differences in the exact branching sites of bilateral vessels or the size or number of arterial branches. Two Saimaa ringed seals had only a tiny foramen for the left internal carotid artery to enter the intracranial space, and the intracranial part of this vessel was short. It did not reach the cerebral arterial circle. The intracranial part of the right internal carotid artery is bifurcated and also supplied the left side of the cerebral arterial circle. Both specimens had aplasia of the left rostral cerebral artery. The intracranial arterial arrangement of Saimaa and Baltic ringed seals reflects the arterial architecture of this body region in terrestrial mammals with little evidence for aquatic adaptations or changes related to thermoregulation.

Trend of cerebral aneurysms over the past two centuries: need for early screening

OBJECTIVE

Cerebral aneurysms (CAs) are linked to variations in the cerebral basal arterial network (CBAN). This study aimed to find the optimal age for screening to detect brain arterial variations and predict aneurysms before rupture.

DESIGN

An observational, quantitative and retrospective research.

SETTING

The study analysed 1127 cases of CAs published from 1761 to 1938. Additionally, CT angiography images of 173-patients at the Royal Adelaide Hospital (RAH), South Australia between 2011 and 2019 were examined for the presence and the location of aneurysms in CBAN.

PARTICIPANTS

The data were collected from patients at RAH and 407 published sources, including males and females across the entire age range, up to 100 years old.

OUTCOME MEASURES AND RESULTS

Data, CAs cases, from 1761 to 1938 included (526 males, 573 females and 28 unknown sexes). The age of these patients varied from 18 months to 89 years (mean age=42, SD=18). Approximately 11.5% of the CAs occurred in patients aged <20 years. Among the 1078 aneurysms whose location was reported, 76% were located in the internal carotid (IC), middle cerebral (MC) and anterior communicating artery complex (AcomAC) regions, while the remaining 24% were in the vertebrobasilar region. Among 173 patients from RAH aged between 18 and 100 years (male=83 and female=90, mean age=60, SD=16), 94% of the CAs were found in the IC, MC and AcomAC regions. The pattern of aneurysm occurrence, as indicated by values at the 25th, 50th and 75th percentiles, along with the minimum and maximum patient ages, has remained consistent from 1761 to 2019.

CONCLUSION

The distribution pattern of CAs in relation to sex, age and locations in the CBAN, remained steady over the last 260 years resulting in risk of strokes early in life. Therefore, early screening for CBAN segment variations is advised for stroke prevention if possible.

A data-driven computational methodology towards a pre-hospital Acute Ischaemic Stroke screening tool using haemodynamics waveforms

BACKGROUND AND OBJECTIVE

Acute Ischaemic Stroke (AIS), a significant global health concern, results from occlusions in cerebral arteries, causing irreversible brain damage. Different type of treatments exist depending on the size and location of the occlusion. Challenges persist in achieving faster diagnosis and treatment, which needs to happen in the first hours after the onset of symptoms to maximize the chances of patient recovery. The current diagnostic pipeline, i.e. "drip and ship", involves diagnostic via advanced imaging tools, only available in large clinical facilities, which poses important delays. This study investigates the feasibility of developing a machine learning model to diagnose and locate occluding blood clots from velocity waveforms, which can be easily be obtained with portable devices such as Doppler Ultrasound. The goal is to explore this approach as a cost-effective and time-efficient alternative to advanced imaging techniques typically available only in large hospitals.

METHODS

Simulated haemodynamic data is used to conduct blood flow simulations representing healthy and different AIS scenarios using a population-based database. A Machine Learning classification model is trained to solve the inverse problem, this is, detect and locate a potentially occluding thrombus from measured waveforms. The classification process involves two steps. First, the region where the thrombus is located is classified into nine groups, including healthy, left or right large vessel occlusion, left or right anterior cerebral artery, and left or right posterior cerebral artery. In a second step, the bifurcation generation of the thrombus location is classified as small, medium, or large vessel occlusion.

RESULTS

The proposed methodology is evaluated for data without noise, achieving a true prediction rate exceeding 95% for both classification steps mentioned above. The inclusion of up to 20% noise reduces the true prediction rate to 80% for region detection and 70% for bifurcation generation detection.

CONCLUSIONS

This study demonstrates the potential effectiveness and efficiency of using haemodynamic data and machine learning to detect and locate occluding thrombi in AIS patients. Although the geometric and topological data used in this study are idealized, the results suggest that this approach could be applicable in real-world situations with appropriate adjustments. Source code is available in https://github.com/ahmetsenemse/Acute-Ischaemic-Stroke-screening-tool-.

Modeling and evaluation of biomechanics and hemodynamic based on patient-specific small intracranial aneurysm using fluid-structure interaction

BACKGROUND AND OBJECTIVE

Rupture of small intracranial aneurysm (IA) often leads to the development of highly fatal clinical syndromes such as subarachnoid hemorrhage. Due to the patient specificity of small IA, there are many difficulties in evaluating the rupture risk of small IA such as multiple influencing factors, high clinical experience requirements and poor reusability.

METHODS

In this study, clinical methods such as transcranial doppler (TCD) and magnetic resonance imaging (MRI) are used to obtain patient-specific parameters, and the fluid-structure interaction method (FSI) is used to model and evaluate the biomechanics and hemodynamics of patient-specific small IA.

RESULTS

The results show that a spiral vortex stably exists in the patient-specific small IA. Due to the small size of the patient-specific small IA, the blood flow velocity still maintains a high value with maximum reaching 3 m/s. The inertial impact of blood flow and vortex convection have certain influence on hemodynamic and biomechanics parameters. They cause three high value areas of WSSM on the patient-specific small IA with maximum of 180 Pa, 130 Pa and 110 Pa, respectively. They also cause two types of WSS concentration points, positive normal stress peak value areas and negative normal stress peak value areas to appear.

CONCLUSION

This paper found that the factors affecting hemodynamic parameters and biomechanical parameters are different. Unlike hemodynamic parameters, biomechanical parameters are also affected by blood pressure in addition to blood flow velocity. This study reveals the relationship between the flow field distribution and changes of patient-specific small IA, biomechanics and hemodynamics.

Cerebral perfusion simulation using realistically generated synthetic trees for healthy and stroke patients

BACKGROUND AND OBJECTIVE

Cerebral vascular diseases are among the most burdensome diseases faced by society. However, investigating the pathophysiology of diseases as well as developing future treatments still relies heavily on expensive in-vivo and in-vitro studies. The generation of realistic, patient-specific models of the cerebrovascular system capable of simulating hemodynamics and perfusion promises the ability to simulate diseased states, therefore accelerating development cycles using in silico studies and opening opportunities for the individual assessment of diseased states, treatment planning, and the prediction of outcomes. By providing a patient-specific, anatomically detailed and validated model of the human cerebral vascular system, we aim to provide the basis for future in silico investigations of the cerebral physiology and pathology.

METHODS

In this retrospective study, a processing pipeline for patient-specific quantification of cerebral perfusion was developed and applied to healthy individuals and a stroke patient. Major arteries are segmented from 3T MR angiography data. A synthetic tree generation algorithm titled tissue-growth based optimization (GBO)1 is used to extend vascular trees beyond the imaging resolution. To investigate the anatomical accuracy of the generated trees, morphological parameters are compared against those of 7 T MRI, 9.4 T MRI, and dissection data. Using the generated vessel model, hemodynamics and perfusion are simulated by solving one-dimensional blood flow equations combined with Darcy flow equations.

RESULTS

Morphological data of three healthy individuals (mean age 47 years ± 15.9 [SD], 2 female) was analyzed. Bifurcation and physiological characteristics of the synthetically generated vessels are comparable to those of dissection data. The inability of MRI based segmentation to resolve small branches and the small volume investigated cause a mismatch in the comparison to MRI data. Cerebral perfusion was estimated for healthy individuals and a stroke patient. The simulated perfusion is compared against Arterial-Spin-Labeling MRI perfusion data. Good qualitative agreement is found between simulated and measured cerebral blood flow (CBF)2. Ischemic regions are predicted well, however ischemia severity is overestimated.

CONCLUSIONS

GBO successfully generates detailed cerebral vascular models with realistic morphological parameters. Simulations based on the resulting networks predict perfusion territories and ischemic regions successfully.

Multi-Dimensional Modeling of Cerebral Hemodynamics: A Systematic Review

The Circle of Willis (CoW) describes the arterial system in the human brain enabling the neurovascular blood supply. Neurovascular diseases like intracranial aneurysms (IAs) can occur within the CoW and carry the risk of rupture, which can lead to subarachnoid hemorrhage. The assessment of hemodynamic information in these pathologies is crucial for their understanding regarding detection, diagnosis and treatment. Multi-dimensional in silico approaches exist to evaluate these hemodynamics based on patient-specific input data. The approaches comprise low-scale (zero-dimensional, one-dimensional) and high-scale (three-dimensional) models as well as multi-scale coupled models. The input data can be derived from medical imaging, numerical models, literature-based assumptions or from measurements within healthy subjects. Thus, the most realistic description of neurovascular hemodynamics is still controversial. Within this systematic review, first, the models of the three scales (0D, 1D, 3D) and second, the multi-scale models, which are coupled versions of the three scales, were discussed. Current best practices in describing neurovascular hemodynamics most realistically and their clinical applicablility were elucidated. The performance of 3D simulation entails high computational expenses, which could be reduced by analyzing solely the region of interest in detail. Medical imaging to establish patient-specific boundary conditions is usually rare, and thus, lower dimensional models provide a realistic mimicking of the surrounding hemodynamics. Multi-scale coupling, however, is computationally expensive as well, especially when taking all dimensions into account. In conclusion, the 0D-1D-3D multi-scale approach provides the most realistic outcome; nevertheless, it is least applicable. A 1D-3D multi-scale model can be considered regarding a beneficial trade-off between realistic results and applicable performance.

Quantitative Evaluation of Hemodynamic Changes After Multiple Intracranial Aneurysms Occlusion Using Computational Fluid Dynamics

OBJECTIVE

Multiple intracranial aneurysms (MIA) are prevalent. This study conducted hemodynamic calculations on MIA to analyze the effects of occlusion of the internal carotid artery (ICA) and middle cerebral artery (MCA) aneurysms on the hemodynamics of other arteries, as well as the issue of the treatment order for these aneurysms.

METHODS

The models of 9 patients with MIA were selected for the study. A computational fluid dynamics model combining 1-dimension and 3-dimension was used to obtain the vascular flow pattern and wall pressure.

RESULTS

There was increased pressure at the MCA and anterior cerebral artery (ACA) after occlusion of the aneurysm at the ICA. However, the pressure at the ICA has hardly changed after the aneurysm occlusion at the MCA. Occlusion of the aneurysm of different sizes at the MCA had almost no impact on the pressure at the ICA and ACA. For small aneurysm, the pressure of the ACA and MCA increases with decreasing size of the aneurysm at the ICA. After occlusion of a large aneurysm at the ICA, the impact on the pressure of the ACA and MCA is almost the same as after occlusion of a medium-sized aneurysm.

CONCLUSIONS

If the treatment order of ICA and MCA aneurysms cannot be determined based on patient factors and aneurysm characteristics, the MCA aneurysm should be treated as a priority.

Real-time model-based cerebral perfusion calculation for ischemic stroke

BACKGROUND AND OBJECTIVES

Clinical diagnosis of ischemic stroke commonly relies on examining cerebral perfusion changes by using computed tomography perfusion (CTP) techniques. However, the radiation dose in CTP is quite higher in comparison to computed tomography angiography (CTA), with associated costs and time.

METHODS

Hence, this study established a lumped-parameter model (LPM) of brain tissue microcirculation (BTM) based on CTA, aiming to achieve real-time calculation of cerebral perfusion. After validation of calculated flow results with clinical data, the BTM-LPM model was used to examine the changes in cerebral perfusion following ischemic stroke, in which the effects of nine anatomical structures of the Circle of Willis (CoW) together with various distribution patterns of stenosis in the feeding arteries were considered.

RESULTS

When compared the calculated flow results from BTM-LPM with the clinically measured data of literature, the mean squared error (MSE) value for the feeding arteries was 3.9 % and its total value for microcirculatory flow in each region was 0.1 %. Notably, the calculation time was 35.6 s. In the case of the CoW missing the left and right posterior communicating artery, a 60 % stenosis of the basilar artery is likely to cause ischemic damage to some temporal and occipital lobes of the right and left hemispheres. While in the case of the CoW missing the anterior communicating artery and the left posterior communicating artery, ischemic damage to the entire frontal lobe and parts of the temporal and parietal lobes of the left hemisphere was found when 80 % stenosis occurred in the left internal carotid artery.

CONCLUSIONS

The BTM-LPM proposed in this study could accurately calculate cerebral perfusion in real time and demonstrated the importance of CoW anatomy in different ischemic injuries to cerebral tissue. The calculated cerebral perfusion would be a reference value for early diagnosis and preoperative planning of different ischemic injuries to cerebral tissue, thereby the BTM-LPM holds great promising for replacing CTP examination.

New morphological parameter for intracranial aneurysms and rupture risk prediction based on artificial neural networks

BACKGROUND

Numerous studies have evaluated the rupture risk of intracranial aneurysms using morphological parameters because of their good predictive capacity. However, the limitation of current morphological parameters is that they do not always allow evaluation of irregularities of intracranial aneurysms. The purpose of this study is to propose a new morphological parameter that can quantitatively describe irregularities of intracranial aneurysms and to evaluate its performance regarding rupture risk prediction.

METHODS

In a retrospective study, conventional morphological parameters (aspect ratio, bottleneck ratio, height-to-width ratio, volume to ostium ratio, and size ratio) and a newly proposed morphological parameter (mass moment of inertia) were calculated for 125 intracranial aneurysms (80 unruptured and 45 ruptured aneurysms). Additionally, hemodynamic parameters (wall shear stress and strain) were calculated using computational fluid dynamics and fluid-structure interaction. Artificial neural networks trained with each parameter were used for rupture risk prediction.

RESULTS

All components of the mass moment of inertia (Ixx, Iyy, and Izz) were significantly higher in ruptured cases than in unruptured cases (p values for Ixx, Iyy, and Izz were 0.032, 0.047, and 0.039, respectively). When the conventional morphological and hemodynamic parameters as well as the mass moment of inertia were considered together, the highest performance for rupture risk prediction was obtained (sensitivity 96.3%; specificity 85.7%; area under the receiver operating characteristic curve 0.921).

CONCLUSIONS

The mass moment of inertia would be a useful parameter for evaluating aneurysm irregularity and hence its risk of rupture. The new approach described here may help clinicians to predict the risk of aneurysm rupture more effectively.

End to end stroke triage using cerebrovascular morphology and machine learning

Background

Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage.

Methods

Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient's cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion's presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient.

Results

The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83.

Conclusion

The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

Approaches to vascular network, blood flow, and metabolite distribution modeling in brain tissue

The cardiovascular system plays a key role in the transport of nutrients, ensuring a continuous supply of all cells of the body with the metabolites necessary for life. The blood supply to the brain is carried out by the large arteries located on its surface, which branch into smaller arterioles that penetrate the cerebral cortex and feed the capillary bed, thereby forming an extensive branching network. The formation of blood vessels is carried out via vasculogenesis and angiogenesis, which play an important role in both embryo and adult life. The review presents approaches to modeling various aspects of both the formation of vascular networks and the construction of the formed arterial tree. In addition, a brief description of models that allows one to study the blood flow in various parts of the circulatory system and the spatiotemporal metabolite distribution in brain tissues is given. Experimental study of these issues is not always possible due to both the complexity of the cardiovascular system and the mechanisms through which the perfusion of all body cells is carried out. In this regard, mathematical models are a good tool for studying hemodynamics and can be used in clinical practice to diagnose vascular diseases and assess the need for treatment.

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy

Intracranial atherosclerotic disease (ICAD) poses a significant risk of subsequent stroke but current prevention strategies are limited. Mechanistic simulations of brain hemodynamics offer an alternative precision medicine approach by utilising individual patient characteristics. For clinical use, however, current simulation frameworks have insufficient validation. In this study, we performed the first quantitative validation of a simulation-based precision medicine framework to assess cerebral hemodynamics in patients with ICAD against clinical standard perfusion imaging. In a retrospective analysis, we used a 0-dimensional simulation model to detect brain areas that are hemodynamically vulnerable to subsequent stroke. The main outcome measures were sensitivity, specificity, and area under the receiver operating characteristics curve (ROC AUC) of the simulation to identify brain areas vulnerable to subsequent stroke as defined by quantitative measurements of relative mean transit time (relMTT) from dynamic susceptibility contrast MRI (DSC-MRI). In 68 subjects with unilateral stenosis >70% of the internal carotid artery (ICA) or middle cerebral artery (MCA), the sensitivity and specificity of the simulation were 0.65 and 0.67, respectively. The ROC AUC was 0.68. The low-to-moderate accuracy of the simulation may be attributed to assumptions of Newtonian blood flow, rigid vessel walls, and the use of time-of-flight MRI for geometric representation of subject vasculature. Future simulation approaches should focus on integrating additional patient data, increasing accessibility of precision medicine tools to clinicians, addressing disease burden disparities amongst different populations, and quantifying patient benefit. Our results underscore the need for further improvement of mechanistic simulations of brain hemodynamics to foster the translation of the technology to clinical practice.

An anatomically detailed arterial-venous network model. Cerebral and coronary circulation

In recent years, several works have addressed the problem of modeling blood flow phenomena in veins, as a response to increasing interest in modeling pathological conditions occurring in the venous network and their connection with the rest of the circulatory system. In this context, one-dimensional models have proven to be extremely efficient in delivering predictions in agreement with in-vivo observations. Pursuing the increase of anatomical accuracy and its connection to physiological principles in haemodynamics simulations, the main aim of this work is to describe a novel closed-loop Anatomically-Detailed Arterial-Venous Network (ADAVN) model. An extremely refined description of the arterial network consisting of 2,185 arterial vessels is coupled to a novel venous network featuring high level of anatomical detail in cerebral and coronary vascular territories. The entire venous network comprises 189 venous vessels, 79 of which drain the brain and 14 are coronary veins. Fundamental physiological mechanisms accounting for the interaction of brain blood flow with the cerebro-spinal fluid and of the coronary circulation with the cardiac mechanics are considered. Several issues related to the coupling of arterial and venous vessels at the microcirculation level are discussed in detail. Numerical simulations are compared to patient records published in the literature to show the descriptive capabilities of the model. Furthermore, a local sensitivity analysis is performed, evidencing the high impact of the venous circulation on main cardiovascular variables.

A network-based model of dynamic cerebral autoregulation

Cerebrovascular diseases continue to be one of the leading causes of morbidity and mortality in humans. Abnormalities in dynamic cerebral autoregulation (dCA) have been implicated in many of these disease conditions. Accurate models are therefore needed to better understand the complex pathophysiology behind impaired dCA. We thus present here a simple framework for modelling a vessel-driven network model of dCA in the microvasculature, as opposed to the conventional compartmental modelling approach. Network models incorporate the actual connectivity and anatomy of the vasculature, thereby allowing us to include and trace changes in the calibre and morphology of individual vessels, investigate the spatial specificity and heterogeneity of the various control mechanisms to help disentangle their contributions, and link the model parameters to the actual network physiology. The proposed control feedback mechanisms are incorporated at the level of the individual vessel, and the dynamic pressure and flow fields are solved for here within a simple vessel network. In response to an upstream pressure drop, the network is found to be able to recover cerebral blood flow (CBF) while exhibiting the characteristic autoregulatory behaviour in terms of changes in vessel calibre and the biphasic flow response. We assess the feasibility of our formulation in larger networks by comparing the simulation results to those obtained using a one-dimensional (1D) model of CBF applied to the same microvasculature network and find that our model results are in very good agreement with the 1D solution, while significantly reducing the computational cost, thus enabling more detailed models of network behaviour to be adopted in the future. Accurate and computationally feasible models of dCA that are more representative of the vasculature can help increase the translatability of haemodynamic models into the clinical environment, which would help develop more informed treatment guidelines for patients with cerebrovascular diseases.

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals.

Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories.

Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR).

Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (&gt;20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean.

Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

A multiscale computational framework to evaluate flow alterations during mechanical thrombectomy for treatment of ischaemic stroke

The treatment of ischaemic stroke increasingly relies upon endovascular procedures known as mechanical thrombectomy (MT), which consists in capturing and removing the clot with a catheter-guided stent while at the same time applying external aspiration with the aim of reducing haemodynamic loads during retrieval. However, uniform consensus on procedural parameters such as the use of balloon guide catheters (BGC) to provide proximal flow control, or the position of the aspiration catheter is still lacking. Ultimately the decision is left to the clinician performing the operation, and it is difficult to predict how these treatment options might influence clinical outcome. In this study we present a multiscale computational framework to simulate MT procedures. The developed framework can provide quantitative assessment of clinically relevant quantities such as flow in the retrieval path and can be used to find the optimal procedural parameters that are most likely to result in a favorable clinical outcome. The results show the advantage of using BGC during MT and indicate small differences between positioning the aspiration catheter in proximal or distal locations. The framework has significant potential for future expansions and applications to other surgical treatments.

In silicostudy of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head

Objective.This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of thisin silicostudy is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation.Approach.We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS).Main results.Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS.Significance.Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.

Modeling Reactive Hyperemia to Better Understand and Assess Microvascular Function: A Review of Techniques

Reactive hyperemia is a well-established technique for the non-invasive evaluation of the peripheral microcirculatory function, measured as the magnitude of limb re-perfusion after a brief period of ischemia. Despite widespread adoption by researchers and clinicians alike, many uncertainties remain surrounding interpretation, compounded by patient-specific confounding factors (such as blood pressure or the metabolic rate of the ischemic limb). Mathematical modeling can accelerate our understanding of the physiology underlying the reactive hyperemia response and guide in the estimation of quantities which are difficult to measure experimentally. In this work, we aim to provide a comprehensive guide for mathematical modeling techniques that can be used for describing the key phenomena involved in the reactive hyperemia response, alongside their limitations and advantages. The reported methodologies can be used for investigating specific reactive hyperemia aspects alone, or can be combined into a computational framework to be used in (pre-)clinical settings.

Efficacy assessment of superficial temporal artery-middle cerebral artery bypass surgery in treating moyamoya disease from a hemodynamic perspective: a pilot study using computational modeling and perfusion imaging

BACKGROUND

Superficial temporal artery-middle cerebral artery (STA-MCA) bypass is a common surgery in treating moyamoya disease (MMD) with occluded MCA. Computational fluid dynamics (CFD) simulation might provide a simple, non-invasive, and low-cost tool to evaluate the efficacy of STA-MCA surgery.

AIM

We aim to quantitatively investigate the treatment efficacy of STA-MCA surgery in improving the blood flow of MMD patients using CFD simulation.

METHODS

This retrospective study included 11 MMD patients with occlusion around proximal MCA who underwent STA-MCA bypass surgery. CFD simulation was performed using patient-specific blood pressure and postoperative artery geometry. The volumetric flow rates of STA and the bypass, average flow velocity in the proximal segment of transcranial bypass, transcranial pressure drop, and transcranial flow resistance were measured and compared with a postoperative increment of cerebral blood flow (CBF) in MCA territories derived from perfusion imaging. Per-branch pressure drop from model inlet to bypass branch outlet was calculated.

RESULTS

The volumetric flow rates of STA and the bypass were 80.84 ± 14.54 mL/min and 46.03 ± 4.21 mL/min. Average flow velocity in proximal bypass, transcranial pressure drop, and transcranial flow resistance were 0.19 ± 0.07 m/s, 3.72 ± 3.10 mmHg, and 6.54 ± 5.65 10^-8 Pa s m^-3. Postoperative mean increment of CBF in MCA territories was 16.03 ± 11.72 mL·100 g^-1·min^-1. Per-branch pressure drop was 10.96 ± 5.59 mmHg and 7.26 ± 4.25 mmHg in branches with and without stenosis.

CONCLUSIONS

CFD simulation results are consistent with CBF observation in verifying the efficacy of STA-MCA bypass, where postoperative stenosis may influence the hemodynamics.

Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model

BACKGROUND

Cerebral aneurysms should be treated before rupture because ruptured aneurysms result in serious disability. Therefore, accurate prediction of rupture risk is important and has been estimated using various hemodynamic factors.

OBJECTIVE

To suggest a new way to predict rupture risk in cerebral aneurysms using a novel deep learning model based on hemodynamic parameters for better decision-making about treatment.

METHODS

A novel convolutional neural network (CNN) model was used for rupture risk prediction retrospectively of 123 aneurysm cases. To include the effect of hemodynamic parameters into the CNN, the hemodynamic parameters were first calculated using computational fluid dynamics and fluid-structure interaction. Then, they were converted into images for training the CNN using a novel approach. In addition, new data augmentation methods were devised to obtain sufficient training data. A total of 53,136 images generated by data augmentation were used to train and test the CNN.

RESULTS

The CNNs trained with wall shear stress (WSS), strain, and combination images had area under the receiver operating characteristics curve values of 0.716, 0.741, and 0.883, respectively. Based on the cut-off values, the CNN trained with WSS (sensitivity: 0.5, specificity: 0.79) or strain (sensitivity: 0.74, specificity: 0.71) images alone was not highly predictive. However, the CNN trained with combination images of WSS and strain showed a sensitivity and specificity of 0.81 and 0.82, respectively.

CONCLUSION

CNN-based deep learning algorithm using hemodynamic factors, including WSS and strain, could be an effective tool for predicting rupture risk in cerebral aneurysms with good predictive accuracy.

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.

Effects of stent shape on focal hemodynamics in intracranial atherosclerotic stenosis: A simulation study with computational fluid dynamics modeling

Background and aims

The shape of a stent could influence focal hemodynamics and subsequently plaque growth or in-stent restenosis in intracranial atherosclerotic stenosis (ICAS). In this preliminary study, we aim to investigate the associations between stent shapes and focal hemodynamics in ICAS, using computational fluid dynamics (CFD) simulations with manually manipulated stents of different shapes.

Methods

We built an idealized artery model, and reconstructed four patient-specific models of ICAS. In each model, three variations of stent geometry (i.e., enlarged, inner-narrowed, and outer-narrowed) were developed. We performed static CFD simulation on the idealized model and three patient-specific models, and transient CFD simulation of three cardiac cycles on one patient-specific model. Pressure, wall shear stress (WSS), and low-density lipoprotein (LDL) filtration rate were quantified in the CFD models, and compared between models with an inner- or outer-narrowed stent vs. an enlarged stent. The absolute difference in each hemodynamic parameter was obtained by subtracting values from two models; a normalized difference (ND) was calculated as the ratio of the absolute difference and the value in the enlarged stent model, both area-averaged throughout the arterial wall.

Results

The differences in focal pressure in models with different stent geometry were negligible (ND<1% for all cases). However, there were significant differences in the WSS and LDL filtration rate with different stent geometry, with ND >20% in a static model. Observable differences in WSS and LDL filtration rate mainly appeared in area adjacent to and immediately distal to the stent. In the transient simulation, the LDL filtration rate had milder temporal fluctuations than WSS.

Conclusions

The stent geometry might influence the focal WSS and LDL filtration rate in ICAS, with negligible effect on pressure. Future studies are warranted to verify the relevance of the changes in these hemodynamic parameters in governing plaque growth and possibly in-stent restenosis in ICAS.

Association between Anatomical Variations of the Circle of Willis and Covert Vascular Brain Injury in the General Population

BACKGROUND AND PURPOSE

The circle of Willis (COW) is a circulatory anastomosis located at the base of the brain. Little is known about the association between covert vascular brain injury and COW configurations in the general population. We explored this relationship in a community-based Chinese sample.

METHODS

A total of 1,055 patients (mean age, 54.8 ± 8.9 years; 36.0% men) without intracranial arterial stenosis were included in the analysis. Magnetic resonance imaging was performed to evaluate the presence of imaging markers of covert vascular brain injury, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), enlarged perivascular spaces, and brain atrophy. Magnetic resonance angiography was used to classify the COW configurations according to the completeness, symmetry, and presence of the fetal posterior cerebral artery (FTP). The association between vascular lesions and variations in COW was analyzed.

RESULTS

Among the 1,055 patients, 104 (9.9%) had a complete COW. Completeness correlated with age (p = 0.001). Incomplete COW was positively associated with WMH severity (OR = 2.071; 95% CI, 1.004-4.270) and CMB presence (OR = 1.542; 95% CI, 1.012-2.348), independent of age and sex. The presence of FTP was associated with lacunes (OR = 1.878; 95% CI, 1.069-3.298), more severe WMHs (OR = 1.739; 95% CI, 1.064-2.842), and less severe enlarged perivascular spaces (OR = 0.562; 95% CI, 0.346-0.915).

CONCLUSIONS

COW configuration was significantly related to various covert vascular brain injuries.

Validation of a cerebral hemodynamic model with personalized calibration in patients with aneurysmal subarachnoid hemorrhage

This study aims to validate a numerical model developed for assessing personalized circle of Willis (CoW) hemodynamics under pathological conditions. Based on 66 computed tomography angiography images, investigations were obtained from 43 acute aneurysmal subarachnoid hemorrhage (aSAH) patients from a local neurovascular center. The mean flow velocity of each artery in the CoW measured using transcranial Doppler (TCD) and simulated by the numerical model was obtained for comparison. The intraclass correlation coefficient (ICC) over all cerebral arteries for TCD and the numerical model was 0.88 (N = 561; 95% CI 0.84-0.90). In a subgroup of patients who had developed delayed cerebral ischemia (DCI), the ICC had decreased to 0.72 but remained constant with respect to changes in blood pressure, Fisher grade, and location of ruptured aneurysm. Our numerical model showed good agreement with TCD in assessing the flow velocity in the CoW of patients with aSAH. In conclusion, the proposed model can satisfactorily reproduce the cerebral hemodynamics under aSAH conditions by personalizing the numerical model with TCD measurements. Clinical trial registration: [http://www.trialregister.nl/], identifier [NL8114].

A coupled atrioventricular-aortic setup for in-vitro hemodynamic study of the systemic circulation: Design, fabrication, and physiological relevancy

In-vitro models of the systemic circulation have gained a lot of interest for fundamental understanding of cardiovascular dynamics and for applied hemodynamic research. In this study, we introduce a physiologically accurate in-vitro hydraulic setup that models the hemodynamics of the coupled atrioventricular-aortic system. This unique experimental simulator has three major components: 1) an arterial system consisting of a human-scale artificial aorta along with the main branches, 2) an artificial left ventricle (LV) sac connected to a programmable piston-in-cylinder pump for simulating cardiac contraction and relaxation, and 3) an artificial left atrium (LA). The setup is designed in such a way that the basal LV is directly connected to the aortic root via an aortic valve, and to the LA via an artificial mitral valve. As a result, two-way hemodynamic couplings can be achieved for studying the effects that the LV, aorta, and LA have on each other. The collected pressure and flow measurements from this setup demonstrate a remarkable correspondence to clinical hemodynamics. We also investigate the physiological relevancies of isolated effects on cardiovascular hemodynamics of various major global parameters found in the circulatory system, including LV contractility, LV preload, heart rate, aortic compliance, and peripheral resistance. Subsequent control over such parameters ultimately captures physiological hemodynamic effects of LV systolic dysfunction, preload (cardiac) diseases, and afterload (arterial) diseases. The detailed design and fabrication of the proposed setup is also provided.

Physiologic Flow Diversion Coiling Technique for Wide-Necked Aneurysms with an Asymmetric Bidirectional Flow at the Aneurysm Neck

PURPOSE

Wide-necked aneurysms in the circle of Willis (CoW) are prone to recur due to reciprocal bidirectional flow. We present a novel concept of coil embolization to prevent recurrence that uses physiologic flow diversion at the CoW.

MATERIALS AND METHODS

We enrolled 14 patients (15 aneurysms) who underwent aneurysm coiling for wide-necked aneurysms with asymmetric bidirectional inflow into the aneurysm. Four patients had recurrent aneurysms after coiling. The concept of physiologic flow diversion included obliterating antegrade flow into the aneurysm sac as well as opposite CoW flow by performing compact coil packing with intentional protrusion out of the aneurysm neck to the communicating part.

RESULTS

Fifteen aneurysms, including 4 recurrent aneurysms, in an anterior communicating artery (n=7), posterior communicating artery (n=5), and tip of the basilar artery (n=3) were treated with coil embolization (n=10) and stent-assisted coiling (n=5). All aneurysms had a wide neck, and the mean largest diameter was 9.0 mm. The mean packing density was 45.1%. Twelve aneurysms were completely occluded, and 3 aneurysms had tiny residual neck remnants. There was neither a neurological event nor recurrence during the mean 12.5 months of follow-up.

CONCLUSION

Wide-necked aneurysms at the CoW tend to recur. As a strategy to prevent a recurrence, physiologic flow diversion can be an option in treating wide-necked aneurysms in the CoW.

Modelling collateral flow and thrombus permeability during acute ischaemic stroke

The presence of collaterals and high thrombus permeability are associated with good functional outcomes after an acute ischaemic stroke. We aim to understand the combined effect of the collaterals and thrombus permeability on cerebral blood flow during an acute ischaemic stroke. A cerebral blood flow model including the leptomeningeal collateral circulation is used to simulate cerebral blood flow during an acute ischaemic stroke. The collateral circulation is varied to capture the collateral scores: absent, poor, moderate and good. Measurements of the transit time, void fraction and thrombus length in acute ischaemic stroke patients are used to estimate thrombus permeability. Estimated thrombus permeability ranges between 10^-7 and 10^-4 mm². Measured flow rates through the thrombus are small and the effect of a permeable thrombus on brain perfusion during stroke is small compared with the effect of collaterals. Our simulations suggest that the collaterals are a dominant factor in the resulting infarct volume after a stroke.

Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging

Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with one-dimensional (1D) reduced-order model (ROM) simulations to generate physically consistent brain hemodynamic parameters with high spatiotemporal resolution. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull's acoustic windows. Our deep learning framework uses in vivo real-time TCD velocity measurements at several locations in the brain combined with baseline vessel cross-sectional areas acquired from 3D angiography images and provides high-resolution maps of velocity, area, and pressure in the entire brain vasculature. We validate the predictions of our model against in vivo velocity measurements obtained via four-dimensional (4D) flow magnetic resonance imaging (MRI) scans. We then showcase the clinical significance of this technique in diagnosing cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocity measurements. We show this capability by generating synthetic blood flow data after cerebral vasospasm at various levels of stenosis. Here, we demonstrate that the physics-based deep learning approach can estimate and quantify the subject-specific cerebral hemodynamic variables with high accuracy despite lacking knowledge of inlet and outlet boundary conditions, which is a significant limitation for the accuracy of the conventional purely physics-based computational models.

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

BACKGROUND AND PURPOSE

Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of the anatomical variations in the Circle of Willis (CoW) and vascular morphology in healthy aging, acute ischemic stroke (AIS) and Alzheimer's Disease (AD).

METHODS

We used our novel automatic method to segment and extract geometric features of the cerebral vasculature from MR angiography scans of 175 healthy subjects, which were used to create a probabilistic atlas of cerebrovasculature and to study normal aging and intersubject variations in CoW anatomy. Subsequently, we quantified and analyzed vascular alterations in 45AIS and 50 AD patients, two prominent cerebrovascular and neurodegenerative disorders.

RESULTS

In the sampled cohort, we determined that the CoW is fully formed in only 35% of healthy adults and found significantly (p < .05) increased tortuosity and fractality, with increasing age and also with disease in both AIS and AD. We also found significantly lower vessel length, volume, and number of branches in AIS patients, as expected. The AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened (p < .05) among healthy, early onset mild AD, and moderate/severe dementia groups.

CONCLUSION

Although our study does not include longitudinal data due to paucity of such datasets, the specific geometric features and quantitative comparisons demonstrate the potential for using vascular morphology as a noninvasive imaging biomarker for neurologic disorders.

Early Diagnosis of Intracranial Internal Carotid Artery Stenosis Using Extracranial Hemodynamic Indices from Carotid Doppler Ultrasound

Atherosclerotic intracranial internal carotid artery stenosis (IICAS) is a leading cause of strokes. Due to the limitations of major cerebral imaging techniques, the early diagnosis of IICAS remains challenging. Clinical studies have revealed that arterial stenosis may have complicated effects on the blood flow’s velocity from a distance. Therefore, based on a patient-specific one-dimensional hemodynamic model, we quantitatively investigated the effects of IICAS on extracranial internal carotid artery (ICA) flow velocity waveforms to identify sensitive hemodynamic indices for IICAS diagnoses. Classical hemodynamic indices, including the peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index (RI), were calculated on the basis of simulations with and without IICAS. In addition, the first harmonic ratio (FHR), which is defined as the ratio between the first harmonic amplitude and the sum of the amplitudes of the 1st−20th order harmonics, was proposed to evaluate flow waveform patterns. To investigate the diagnostic performance of the indices, we included 52 patients with mild-to-moderate IICAS (<70%) in a case−control study and considered 24 patients without stenosis as controls. The simulation analyses revealed that the existence of IICAS dramatically increased the FHR and decreased the PSV and EDV in the same patient. Statistical analyses showed that the average PSV, EDV, and RI were lower in the stenosis group than in the control group; however, there were no significant differences (p > 0.05) between the two groups, except for the PSV of the right ICA (p = 0.011). The FHR was significantly higher in the stenosis group than in the control group (p < 0.001), with superior diagnostic performance. Taken together, the FHR is a promising index for the early diagnosis of IICAS using carotid Doppler ultrasound methods.