Introduction

Studies have reported a correlation between blood flow dynamics in the cardiac cycle and vascular diseases, but research to analyze the dynamic changes of flow in cerebral aneurysms is limited. This quantitative study investigates the temporal changes in flow during a cardiac cycle (flow waveform) in different regions of aneurysms and their association with aneurysm rupture.

Methods

Twelve ruptured and 29 unruptured aneurysms from the internal carotid artery-ophthalmic artery segment were studied. Patient-specific aneurysm data were implemented to simulate blood flow. The temporal flow changes at different regions of the aneurysm were recorded to compare the flow waveforms.

Results

In more than 60 % of the cases, peak flow in the aneurysm sac occurred after peak flow in the artery. Flow rate varied among cases and no correlation with rupture, aneurysm flow rate, and aneurysm size was found. Higher pulsatility within aneurysm sacs was found when comparing with the parent artery (P < 0.001). Pulsatility was high throughout ruptured aneurysms, but increased from neck to dome in unruptured ones (P = 0.021). Significant changes between inflow and outflow flow profile were found in unruptured aneurysms (P = 0.023), but not in ruptured aneurysms.

Conclusion

Quantitative analysis which considers temporal blood flow changes appears to provide additional information which is not apparent from aneurysmal flow at a single time point (i.e., peak of systole). By considering the flow waveform throughout the cardiac cycle, statistically significant differences were found between ruptured and unruptured cases - for flow profile, pulsatility and timing of peak flow.

GOAL: Identifying population differences can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital. METHODS: We build a machine learning model to visualize gender differences in the circle of Willis (CoW), an integral part of the brains vasculature. We start with a dataset of 570 individuals and process them for analysis using 389 for the final analysis. RESULTS: We find statistical differences between male and female patients in one image plane and visualize where they are. We can see differences between the right and left-hand sides of the brain confirmed using Support Vector Machines (SVM). CONCLUSION: This process can be applied to detect population variations in the vasculature automatically. SIGNIFICANCE: It can guide debugging and inferring complex machine learning algorithms such as SVM and deep learning models.

BACKGROUND AND PURPOSE: Since growing intracranial aneurysms (IA) are more likely to rupture, detecting growth is an important part of unruptured IA follow-up. Recent studies have consistently shown that detecting IA growth can be challenging, especially in smaller aneurysms. In this study, we present an automated computational method to assist detecting aneurysm growth. MATERIALS AND METHODS: An analysis program, Aneurysm Growth Evaluation & Detection (AGED) based on IA images was developed. To verify the program can satisfactorily detect clinical aneurysm growth, we performed this comparative study using clinical determinations of growth during IA follow-up as a gold standard. Patients with unruptured, saccular IA followed by diagnostic brain CTA to monitor IA progression were reviewed. 48 IA image series from twenty longitudinally-followed ICA IA were analyzed using AGED. A set of IA morphologic features were calculated. Nonparametric statistical tests and ROC analysis were performed to evaluate the performance of each feature for growth detection. RESULTS: The set of automatically calculated morphologic features demonstrated comparable results to standard, manual clinical IA growth evaluation. Specifically, automatically calculated HMAX was superior (AUC = 0.958) at distinguishing growing and stable IA, followed by V, and SA (AUC = 0.927 and 0.917, respectively). CONCLUSION: Our findings support automatic methods of detecting IA growth from sequential imaging studies as a useful adjunct to standard clinical assessment. AGED-generated growth detection shows promise for characterization and detection of IA growth and time-saving comparing with manual measurements.

Various anatomical parameters affect on intra-aneurysmal hemodynamics. Nevertheless, how the shapes of real patient aneurysms affect on their intra-aneurysmal hemodynamics remains unanswered. Quantitative computational fluid dynamics simulation was conducted using eight patients’ angiograms of internal carotid artery–ophthalmic artery aneurysms. The mean size of the intracranial aneurysms was 11.5 mm (range 5.8 to 19.9 mm). Intra-aneurysmal blood flow velocity and wall shear stress (WSS) were collected from three measurement planes in each aneurysm dome. The correlation coefficients (r) were obtained between hemodynamic values (flow velocity and WSS) and the following anatomical parameters: averaged dimension of aneurysm dome, the largest aneurysm dome dimension, aspect ratio, and dome–neck ratio. Negative linear correlations were observed between the averaged dimension of aneurysm dome and intra-aneurysmal flow velocity (r = −0.735) and also WSS (r = −0.736). The largest dome diameter showed a negative correlation with intra-aneurysmal flow velocity (r = −0.731) and WSS (r = −0.496). The aspect ratio demonstrated a weak negative correlation with the intra-aneurysmal flow velocity (r = −0.381) and WSS (r = −0.501). A clear negative correlation was seen between the intra-aneurysmal flow velocity and the dome–neck ratio (r = −0.708). A weak negative correlation is observed between the intra-aneurysmal WSS and the dome–neck ratio (r = −0.392). The aneurysm dome size showed a negative linear correlation with intra-aneurysmal flow velocity and WSS. Wide-necked aneurysm geometry was associated with faster intra-aneurysmal flow velocity.

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications toward a learning health system paradigm.

A comparative analysis between intravascular guidewire-obtained and computational fluid dynamic (CFD) flow velocity and pressure data using simplified carotid stenosis models was performed. This information was used to evaluate the viability of using guidewire pressure data to provide inlet conditions for CFD flow, and to study the relationship between stenotic length and hemodynamic behavior. Carotid stenosis models differing in diameter and length were prepared and connected to a vascular pulsatile flow simulator. Time-dependent flow velocity and pressure measurements were taken by microcatheter guidewires and compared with CFD data. Guidewire and CFD-generated pressure profiles matched closely in all measurement locations. The guidewire was unable to reliably measure flow velocity at areas associated with higher CFD flow velocities (r = 0.92). CFD results showed that an increased length of stenosis generated expansive regions of elevated wall shear stress (WSS) within and distal to the stenosis. Low WSS was found immediately outside the stenosis outlet. An increase in stenotic length produced higher flow velocities with minimal lengthening of the distal high velocity flow jet due to faster dissipation of translational kinetic energy through turbulence. We found the accuracy of guidewire-obtained velocity measurements is limited to regions unaffected by disturbed flow. WSS and turbulence behavior distal to the stenosis may be important markers to evaluate the severity of atherosclerotic progression as a function of stenotic length.

BACKGROUND: While image-derived predictors of intracranial aneurysm (IA) rupture have been well-explored, current understanding of IA growth is limited. Pulsatility index (PI) and wall shear stress pulsatility index (WSSPI) are important metrics measuring temporal hemodynamic instability. However, they have not been investigated in IA growth research. The present study seeks to verify reliable predictors of IA growth with comparative analyses of several important morphological and hemodynamic metrics between stable and growing cases among a group of unruptured IAs. METHODS: Using 3D images, vascular models of 16 stable and 20 growing cases were constructed and verified using Geodesic techniques. With an overall mean follow-up period of 25 months, cases exhibiting a 10% or higher increase in diameter were considered growing. Patient-specific, pulsatile simulations were performed, and hemodynamic calculations were computed at 5 important regions of each aneurysm (inflow artery, aneurysm neck, body, dome, and outflow artery). Index values were compared between growing and stable IAs using ANCOVA controlling for aneurysm diameter. Stepwise multiple logistic regression and ROC analyses were conducted to investigate predictive models of IA growth. RESULTS: Compared to stable IAs, growing IAs exhibited significantly higher intrasaccular PI, intrasaccular WSSPI, intrasaccular spatial flow rate deviation, and intrasaccular spatial wall shear stress (WSS) deviation. Stepwise logistic regression analysis revealed a significant predictive model involving PI at aneurysm body, WSSPI at inflow artery, and WSSPI at aneurysm body. CONCLUSIONS: Our results showed that high degree of hemodynamic variations within IAs is linked to growth, even after controlling for morphological parameters. Further, evaluation of PI in conjunction with WSSPI yielded a highly accurate predictive model of IA growth. Upon validation in future cohorts, these metrics may aid in early identification of IA growth and current understanding of IA remodeling mechanism.

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications towards a learning health system paradigm.Graphical abstract