@neurIST complex information processing toolchain for the integrated management of cerebral aneurysms

Off-label in-silico flow diverter performance assessment in posterior communicating artery aneurysms

BACKGROUND

The posterior communicating artery (PComA) is among the most common intracranial aneurysm locations, but flow diverter (FD) treatment with the widely used pipeline embolization device (PED) remains an off-label treatment that is not well understood. PComA aneurysm flow diversion is complicated by the presence of fetal posterior circulation (FPC), which has an estimated prevalence of 4-29% and is more common in people of black (11.5%) than white (4.9%) race. We present the FD-PComA in-silico trial (IST) into FD treatment performance in PComA aneurysms. ISTs use computational modeling and simulation in cohorts of virtual patients to evaluate medical device performance.

METHODS

We modeled FD treatment in 118 virtual patients with 59 distinct PComA aneurysm anatomies, using computational fluid dynamics to assess post-treatment outcome. Boundary conditions were prescribed to model the effects of non-fetal and FPC, allowing for comparison between these subgroups.

RESULTS

FD-PComA predicted reduced treatment success in FPC patients, with an average aneurysm space and time-averaged velocity reduction of 67.8% for non-fetal patients and 46.5% for fetal patients (P<0.001). Space and time-averaged wall shear stress on the device surface was 29.2 Pa averaged across fetal patients and 23.5 Pa across non-fetal (P<0.05) patients, suggesting FD endothelialization may be hindered in FPC patients. Morphological variables, such as the size and shape of the aneurysm and PComA size, did not affect the treatment outcome.

CONCLUSIONS

FD-PComA had significantly lower treatment success rates in PComA aneurysm patients with FPC. We suggest that FPC patients should be treated with an alternative to single PED flow diversion.

Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks

Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high-fidelity computational models and potentially achieve large gains in execution speed. Machine learning (ML) using neural networks has been used to overcome limitations of traditional ROM techniques when applied to nonlinear problems, which has led to the recent development of reduced order models augmented by machine learning (ML-ROMs). However, the performance of ML-ROMs is yet to be widely evaluated in realistic applications and questions remain regarding the optimal design of ML-ROMs. In this study, we investigate the application of a non-intrusive parametric ML-ROM to a nonlinear, time-dependent fluid dynamics problem in a complex 3D geometry. We construct the ML-ROM using POD for dimensionality reduction and neural networks for interpolation of the ROM coefficients. We compare three different network designs in terms of approximation accuracy and performance. We test our ML-ROM on a flow problem in intracranial aneurysms, where flow variability effects are important when evaluating rupture risk and simulating treatment outcomes. The best-performing network design in our comparison used a two-stage POD reduction, a technique rarely used in previous studies. The best-performing ROM achieved mean test accuracies of 98.6% and 97.6% in the parent vessel and the aneurysm, respectively, while providing speed-up factors of the order10 5 .

Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model

Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.

Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first in silico observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time and shear rate, thus addressing the second question. We then address the third question by utilizing this calibrated model to provide new insight into the effects of hypertension on spontaneous thrombosis. We demonstrate how a mechanistic thrombosis model calibrated on an intracranial aneurysm cohort can help estimate spontaneous thrombosis prevalence in a broader aneurysm population. This study is enabled through a fully automatic multi-scale modeling pipeline. We use the clinical spontaneous thrombosis data as an indirect population-level validation of a complex computational modeling framework. Furthermore, our framework allows exploration of the influence of hypertension in spontaneous thrombosis. This lays the foundation for in silico clinical trials of cerebrovascular devices in high-risk populations, e.g., assessing the performance of flow diverters in aneurysms for hypertensive patients.

Shape Trumps Size: Image-Based Morphological Analysis Reveals That the 3D Shape Discriminates Intracranial Aneurysm Disease Status Better Than Aneurysm Size

Background

To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status.

Methods

3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms.

Results

The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies ZNsurf (AUC = 0.80) and the modified writhe-index W¯meanL1 (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases.

Conclusions

Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms.

Cerebral Aneurysm Occlusion at 12-Month Follow-Up After Flow-Diverter Treatment: Statistical Modeling for V&V With Real-World Data

Background: Flow-Diverter (FD) porosity has been pointed as a critical factor in the occlusion of cerebral aneurysms after treatment.

Objective: Verification and Validation of computational models in terms of predictive capacity, relating FD porosity and occlusion after cerebral aneurysms treatment.

Methods: Sixty-four aneurysms, with pre-treatment and follow-up images, were considered. Patient demographics and aneurysm morphological information were collected. The computational simulation provided by ANKYRAS provided FD porosity, expansion, and mesh angle. FD occlusion was assessed and recorded from follow-up images. Multiple regression Logit and analysis of covariance (ANCOVA) models were used to model the data with both categorical and continuous models.

Results: Occlusion of the aneurysm after 12 months was affected by aneurysm morphology but not by FD mesh morphology. A Time-To-Occlusion (TTO) of 6.92 months on average was observed with an SE of 0.24 months in the aneurysm population surveyed. TTO was estimated with statistical significance from the resulting model for the data examined and was capable of explaining 92% of the data variation.

Conclusions: Porosity was found to have the most correction power when assessing TTO, proving its importance in the process of aneurysm occlusion. Still, further Verification and Validation (V&V) of treatment simulation in more extensive, multi-center, and randomized databases is required.

In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials

The cost of clinical trials is ever-increasing. In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower these costs. We present the flow diverter performance assessment (FD-PASS) in-silico trial, which models the treatment of intracranial aneurysms in 164 virtual patients with 82 distinct anatomies with a flow-diverting stent, using computational fluid dynamics to quantify post-treatment flow reduction. The predicted FD-PASS flow-diversion success rates replicate the values previously reported in three clinical trials. The in-silico approach allows broader investigation of factors associated with insufficient flow reduction than feasible in a conventional trial. Our findings demonstrate that in-silico trials of endovascular medical devices can: (i) replicate findings of conventional clinical trials, and (ii) perform virtual experiments and sub-group analyses that are difficult or impossible in conventional trials to discover new insights on treatment failure, e.g. in the presence of side-branches or hypertension.

In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower costs. Here, the authors present the flow diverter performance assessment in-silico trial, which models the treatment of intracranial aneurysms with a flow-diverting stent.

Modeling intracranial aneurysm stability and growth: an integrative mechanobiological framework for clinical cases

We present a novel patient-specific fluid-solid-growth framework to model the mechanobiological state of clinically detected intracranial aneurysms (IAs) and their evolution. The artery and IA sac are modeled as thick-walled, non-linear elastic fiber-reinforced composites. We represent the undulation distribution of collagen fibers: the adventitia of the healthy artery is modeled as a protective sheath whereas the aneurysm sac is modeled to bear load within physiological range of pressures. Initially, we assume the detected IA is stable and then consider two flow-related mechanisms to drive enlargement: (1) low wall shear stress; (2) dysfunctional endothelium which is associated with regions of high oscillatory flow. Localized collagen degradation and remodelling gives rise to formation of secondary blebs on the aneurysm dome. Restabilization of blebs is achieved by remodelling of the homeostatic collagen fiber stretch distribution. This integrative mechanobiological modelling workflow provides a step towards a personalized risk-assessment and treatment of clinically detected IAs.

Shear stress rosettes capture the complex flow physics in diseased arteries

Wall shear stress (WSS) is an important parameter in arterial mechanobiology. Various flow metrics, such as time averaged WSS (TAWSS), oscillatory shear index (OSI), and transWSS, have been used to characterize and relate possible WSS variations in arterial diseases like aneurysms and atherosclerosis. We use a graphical representation of WSS using shear rosettes to map temporal changes in the flow dynamics during a cardiac cycle at any spatial location on the vessel surface. The presence of secondary flows and flow reversals can be interpreted directly from the shape of the shear rosette. The mean WSS is given by the rosette centroid, the OSI by the splay around the rosette origin, and the transWSS by its width. We define a new metric, anisotropy ratio (AR), based on the ratio of the length to width of the shear rosette, to capture flow bi-directionality. We characterized the flow physics in controls and patient specific geometries of the ascending aorta (AA) and internal carotid artery (ICA) that have fundamentally different flow dynamics due to differences in the Reynolds and Womersley numbers. The differences in the flow dynamics are well reflected in the shapes of the WSS rosettes and the corresponding flow metrics.

Cardiovascular models for personalised medicine: Where now and where next?

The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and 'where-next' steps and challenges discussed.

Role of distal cerebral vasculature in vessel constriction after aneurysm treatment with flow diverter stents

BACKGROUND

Treatment of intracranial aneurysms with flow diverter stent (FDS) procedures can lead to caliber changes of jailed vessels. The reason some branches remain unchanged and others are affected by narrowing remains unknown.

OBJECTIVE

To investigate the influence of resistance to flow from distal vasculature on stent-induced hemodynamic modifications affecting bifurcating vessels.

MATERIALS AND METHODS

Radiological images and demographic data were acquired for 142 aneurysms treated with a FDS. Vascular resistance was estimated from patient-specific anatomic data. Correlation analysis was used to identify correspondence between anatomic data and clinical outcome. Computational Fluid Dynamics was performed on a typical patient-specific model to evaluate the influence of FDS on flow. Relevant hemodynamic variables along the bifurcating vessels were quantitatively analyzed and validated with in vitro data obtained using power Doppler ultrasound.

RESULTS

Statistical analysis showed a correlation between clinical outcome and FDS resistance to flow considering overall jailed vessel vascular resistance (r=0.5, P<0.001). Computational predictions of blood flow showed that hemodynamics is minimally affected by FDS treatment in the ophthalmic artery.

CONCLUSIONS

Jailed vessels are affected by narrowing when resistance to flow from the FDS constitutes a larger proportion of the overall vessel resistance to flow. This knowledge may contribute to better understanding of intracranial hemodynamics after a FDS procedure and reinforce indications for flow diversion in the treatment of intracranial aneurysms.

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

BACKGROUND

For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions.

METHODS

Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model's performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts.

RESULTS

When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model's prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14).

CONCLUSIONS

The model's performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.

Early Results in Flow Diverter Sizing by Computational Simulation: Quantification of Size Change and Simulation Error Assessment

BACKGROUND

Sizing of flow diverters (FDs) stent in the treatment of intracranial aneurysms is a challenging task due to the change of stent length after implantation.

OBJECTIVE

To quantify the size change and assess the error in length prediction in 82 simulated FD deployments.

METHODS

Eighty-two consecutive patients treated with FDs were retrospectively analyzed. Implanted FD length was measured from angiographic images and compared to the nominal sizes of the implanted device. Length change was obtained by subtracting the nominal length from the real length and dividing by the nominal length. Implanted devices were simulated on 3-dimensional models of each patient. Simulation error was obtained by subtracting real length from simulated length and dividing by the real length of the FD. Subanalysis was done using ANOVA. Statistical significance was set to P < .05, and bootstrap resampling was used.

RESULTS

When assessing the length change of the FD after implantation, changes of 30% in average and up to 80% with reference to the nominal length of the device were observed. The simulation results showed a lower error of 3.52% in average with a maximum of 30%. Paired t-test showed nonsignificant differences between measured and real length (P = .07, with the mean of differences at 0.45 mm, 95% confidence interval [-0.950 0.038]).

CONCLUSION

Nominal length is not an accurate sizing metric when choosing the size of an FD irrespective of the brand and manufacturer. Good estimation of the final length of the stent after deployment as expressed by an error of 3.5% in average.

Thrombosis in Cerebral Aneurysms and the Computational Modeling Thereof: A Review

Thrombosis is a condition closely related to cerebral aneurysms and controlled thrombosis is the main purpose of endovascular embolization treatment. The mechanisms governing thrombus initiation and evolution in cerebral aneurysms have not been fully elucidated and this presents challenges for interventional planning. Significant effort has been directed towards developing computational methods aimed at streamlining the interventional planning process for unruptured cerebral aneurysm treatment. Included in these methods are computational models of thrombus development following endovascular device placement. The main challenge with developing computational models for thrombosis in disease cases is that there exists a wide body of literature that addresses various aspects of the clotting process, but it may not be obvious what information is of direct consequence for what modeling purpose (e.g., for understanding the effect of endovascular therapies). The aim of this review is to present the information so it will be of benefit to the community attempting to model cerebral aneurysm thrombosis for interventional planning purposes, in a simplified yet appropriate manner. The paper begins by explaining current understanding of physiological coagulation and highlights the documented distinctions between the physiological process and cerebral aneurysm thrombosis. Clinical observations of thrombosis following endovascular device placement are then presented. This is followed by a section detailing the demands placed on computational models developed for interventional planning. Finally, existing computational models of thrombosis are presented. This last section begins with description and discussion of physiological computational clotting models, as they are of immense value in understanding how to construct a general computational model of clotting. This is then followed by a review of computational models of clotting in cerebral aneurysms, specifically. Even though some progress has been made towards computational predictions of thrombosis following device placement in cerebral aneurysms, many gaps still remain. Answering the key questions will require the combined efforts of the clinical, experimental and computational communities.

The Role of Hemodynamics in Intracranial Bifurcation Arteries after Aneurysm Treatment with Flow-Diverter Stents

BACKGROUND AND PURPOSE

Treatment of intracranial bifurcation aneurysms with flow-diverter stents can lead to caliber changes of the distal vessels in a subacute phase. This study aims to evaluate whether local anatomy and flow disruption induced by flow-diverter stents are associated with vessel caliber changes in intracranial bifurcations.

MATERIALS AND METHODS

Radiologic images and demographic data were acquired for 25 patients with bifurcation aneurysms treated with flow-diverter stents. Whisker plots and Mann-Whitney rank sum tests were used to evaluate if anatomic data and caliber changes could be linked. Symmetry/asymmetry were defined as diameter ratio 1 = symmetric and diameter ratio <1 = asymmetric. Computational fluid dynamics was performed on idealized and patient-specific anatomies to evaluate flow changes induced by flow-diverter stents in the jailed vessel.

RESULTS

Statistical analysis identified a marked correspondence between asymmetric bifurcation and caliber change. Symmetry ratios were lower for cases showing narrowing or subacute occlusion (medium daughter vessel diameter ratio = 0.59) compared with cases with posttreatment caliber conservation (medium daughter vessel diameter ratio = 0.95). Computational fluid dynamics analysis in idealized and patient-specific anatomies showed that wall shear stress in the jailed vessel was more affected when flow-diverter stents were deployed in asymmetric bifurcations (diameter ratio <0.65) and less affected when deployed in symmetric anatomies (diameter ratio ∼1.00).

CONCLUSIONS

Anatomic data analysis showed statistically significant correspondence between caliber changes and bifurcation asymmetry characterized by diameter ratio <0.7 (P < .001). Similarly, computational fluid dynamics results showed the highest impact on hemodynamics when flow-diverter stents are deployed in asymmetric bifurcations (diameter ratio <0.65) with noticeable changes on wall sheer stress fields. Further research and clinical validation are necessary to identify all elements involved in vessel caliber changes after flow-diverter stent procedures.

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms

Background

Using computational fluid dynamics (CFD) to compute the hemodynamics in cerebral aneurysms has received much attention in the last decade. The usability of these methods depends on the quality of the computations, highlighted in recent discussions. The purpose of this study is to investigate the convergence of common hemodynamic indicators with respect to numerical resolution.

Methods

38 middle cerebral artery bifurcation aneurysms were studied at two different resolutions (one comparable to most studies, and one finer). Relevant hemodynamic indicators were collected from two of the most cited studies, and were compared at the two refinements. In addition, correlation to rupture was investigated.

Results

Most of the hemodynamic indicators were very well resolved at the coarser resolutions, correlating with the finest resolution with a correlation coefficient >0.95. The oscillatory shear index (OSI) had the lowest correlation coefficient of 0.83. A logarithmic Bland-Altman plot revealed noticeable variations in the proportion of the aneurysm under low shear, as well as in spatial and temporal gradients not captured by the correlation alone.

Conclusion

Statistically, hemodynamic indicators agree well across the different resolutions studied here. However, there are clear outliers visible in several of the hemodynamic indicators, which suggests that special care should be taken when considering individual assessment.

Computer-Assisted Adjuncts for Aneurysmal Morphologic Assessment: Toward More Precise and Accurate Approaches

Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician's experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.

Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability

Adverse wall shear stress (WSS) patterns are known to play a key role in the localisation, formation, and progression of intracranial aneurysms (IAs). Complex region-specific and time-varying aneurysmal WSS patterns depend both on vascular morphology as well as on variable systemic flow conditions. Computational fluid dynamics (CFD) has been proposed for characterising WSS patterns in IAs; however, CFD simulations often rely on deterministic boundary conditions that are not representative of the actual variations in blood flow. We develop a data-driven statistical model of internal carotid artery (ICA) flow, which is used to generate a virtual population of waveforms used as inlet boundary conditions in CFD simulations. This allows the statistics of the resulting aneurysmal WSS distributions to be computed. It is observed that ICA waveform variations have limited influence on the time-averaged WSS (TAWSS) on the IA surface. In contrast, in regions where the flow is locally highly multidirectional, WSS directionality and harmonic content are strongly affected by the ICA flow waveform. As a consequence, we argue that the effect of blood flow variability should be explicitly considered in CFD-based IA rupture assessment to prevent confounding the conclusions.

Precision Imaging: more descriptive, predictive and integrative imaging

Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.

Transitional flow in aneurysms and the computation of haemodynamic parameters

Haemodynamic forces appear to play an influential role in the evolution of aneurysms. This has led to numerous studies, usually based on computational fluid dynamics. Their focus is predominantly on the wall shear stress (WSS) and associated derived parameters, attempting to find correlations between particular patterns of haemodynamic indices and regions subjected to disease formation and progression. The indices are generally determined by integration of flow properties over a single cardiac cycle. In this study, we illustrate that in some cases the transitional flow in aneurysms can lead to significantly different WSS distributions in consecutive cardiac cycles. Accurate determination of time-averaged haemodynamic indices may thus require simulation of a large number of cycles, which contrasts with the common approach to determine parameters using data from a single cycle. To demonstrate the role of transitional flow, two exemplary cases are considered: flow in an abdominal aortic aneurysm and in an intracranial aneurysm. The key differences that are observed between these cases are explained in terms of the integral timescale of the transitional flows in comparison with the cardiac cycle duration: for relatively small geometries, transients will decay before the next cardiac cycle. In larger geometries, transients are still present when the systolic phase produces new instabilities. These residual fluctuations serve as random initial conditions and thus seed different flow patterns in each cycle. To judge whether statistics are converged, the derived indices from at least two successive cardiac cycles should be compared.

Computational fluid dynamics modelling in cardiovascular medicine

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards ‘digital patient’ or ‘virtual physiological human’ representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.

AView: An Image-based Clinical Computational Tool for Intracranial Aneurysm Flow Visualization and Clinical Management

Intracranial aneurysms (IAs) occur in around 3% of the entire population. IA rupture is responsible for the most devastating type of hemorrhagic strokes, with high fatality and disability rates as well as healthcare costs. With increasing detection of unruptured aneurysms, clinicians are routinely faced with the dilemma whether to treat IA patients and how to best treat them. Hemodynamic and morphological characteristics are increasingly considered in aneurysm rupture risk assessment and treatment planning, but currently no computational tools allow routine integration of flow visualization and quantitation of these parameters in clinical workflow. In this paper, we introduce AView, a prototype of a clinician-oriented, integrated computation tool for aneurysm hemodynamics, morphology, and risk and data management to aid in treatment decisions and treatment planning in or near the procedure room. Specifically, we describe how we have designed the AView structure from the end-user's point of view, performed a pilot study and gathered clinical feedback. The positive results demonstrate AView's potential clinical value on enhancing aneurysm treatment decision and treatment planning.

Modeling of the acute effects of primary hypertension and hypotension on the hemodynamics of intracranial aneurysms

Hemodynamics is a risk factor in intracranial aneurysms (IA). Hypertension and pharmacologically induced hypotension are common in IA patients. This study investigates how hypertension and hypotension may influence aneurysmal hemodynamics. Images of 23 IAs at typical locations were used to build patient-specific Computational Fluid Dynamics models. The effects of hypotension and hypertension were simulated through boundary conditions by modulating the normotensive flow and pressure waveforms, in turn produced by a 1D systemic vascular model. Aneurysm location and flow pattern types were used to categorize the influence of hypotension and hypertension on relevant flow variables (velocity, pressure and wall shear stress). Results indicate that, compared to other locations, vertebrobasilar aneurysms (VBA) are more sensitive to flow changes. In VBAs, space-averaged velocity at peak systole increased by 30% in hypertension (16-21% in other locations). Flow in VBAs in hypotension decreased by 20% (10-13% in other locations). Momentum-driven hemodynamic types were also more affected by hypotension and hypertension, than shear-driven types. This study shows how patient-specific modeling can be effectively used to identify location-specific flow patterns in a clinically-relevant study, thus reinforcing the role played by modeling technologies in furthering our understanding of cardiovascular disease, and their potential in future healthcare.

Intra-aneurysmal flow patterns: illustrative comparison among digital subtraction angiography, optical flow, and computational fluid dynamics

BACKGROUND AND PURPOSE

Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms.

MATERIALS AND METHODS

A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated.

RESULTS

We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles.

CONCLUSIONS

The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.

Modelling the influence of endothelial heterogeneity on the progression of arterial disease: application to abdominal aortic aneurysm evolution

We sophisticate a fluid-solid growth computational framework for modelling aneurysm evolution. A realistic structural model of the arterial wall is integrated into a patient-specific geometry of the vasculature. This enables physiologically representative distributions of haemodynamic stimuli, obtained from a rigid-wall computational fluid dynamics analysis, to be linked to growth and remodelling algorithms. Additionally, a quasistatic structural analysis quantifies the cyclic deformation of the arterial wall so that collagen growth and remodelling can be explicitly linked to the cyclic deformation of vascular cells. To simulate aneurysm evolution, degradation of elastin is driven by reductions in wall shear stress (WSS) below homeostatic thresholds. Given that the endothelium exhibits spatial and temporal heterogeneity, we propose a novel approach to define the homeostatic WSS thresholds: We allow them to be spatially and temporally heterogeneous. We illustrate the application of this novel fluid-solid growth framework to model abdominal aortic aneurysm (AAA) evolution and to examine how the influence of the definition of the WSS homeostatic threshold influences AAA progression. We conclude that improved understanding and modelling of the endothelial heterogeneity is important for modelling aneurysm evolution and, more generally, other vascular diseases where haemodynamic stimuli play an important role.

AneuSearch: a software prototype for intracranial aneurysm searching and clinical decision support

PURPOSE

Clinical decisions for treating intracranial aneurysms (IA) require integrating information in various forms and from multiple sources. We aim to establish a framework namely AneuSearch to integrate relevant information in IA management and also allow for efficient IA searching based on carefully designed criteria.

METHODS

The backbone of AneuSearch is an open-source three-tier DICOM image management system called DCM4Chee, which is a Java implementation for PACS. A supplementary database (AneuSearchDB) was developed to contain morphological features, hemodynamic and histological data. The relational tables in AneuSearchDB correspond to the most fundamental questions raised by neurosurgeons during IA treatment. The system was developed through collaborations between bioengineers and neurosurgeons.

RESULTS

The prototype software has been deployed to computers in a Mianyang Central Hospital in China. Currently, the system contains the data of 105 IA patients, seven hemodynamic simulation results and nine histological section images. This system was queried as per given criteria and can also provide blood flow data after running an external computational fluid dynamics software.

CONCLUSIONS

The prototype software provides a novel tool to IA management. Future works include incorporating IA treatment criteria in IA rupture risk assessment.

RADStation3G: a platform for cardiovascular image analysis integrating PACS, 3D+t visualization and grid computing

RADStation3G is a software platform for cardiovascular image analysis and surgery planning. It provides image visualization and management in 2D, 3D and 3D+t; data storage (images or operational results) in a PACS (using DICOM); and exploitation of patients' data such as images and pathologies. Further, it provides support for computationally expensive processes with grid technology. In this article we first introduce the platform and present a comparison with existing systems, according to the platform's modules (for cardiology, angiology, PACS archived enriched searching and grid computing), and then RADStation3G is described in detail.

Investigating the influence of haemodynamic stimuli on intracranial aneurysm inception

We propose a novel method to reconstruct the hypothetical geometry of the healthy vasculature prior to intracranial aneurysm (IA) formation: a Frenet frame is calculated along the skeletonization of the arterial geometry; upstream and downstream boundaries of the aneurysmal segment are expressed in terms of the local Frenet frame basis vectors; the hypothetical healthy geometry is then reconstructed by propagating a closed curve along the skeleton using the local Frenet frames so that the upstream boundary is smoothly morphed into the downstream boundary. This methodology takes into account the tortuosity of the arterial vasculature and requires minimal user subjectivity. The method is applied to 22 clinical cases depicting IAs. Computational fluid dynamic simulations of the vasculature without IA are performed and the haemodynamic stimuli in the location of IA formation are examined. We observe that locally elevated wall shear stress (WSS) and gradient oscillatory number (GON) are highly correlated (20/22 for WSS and 19/22 for GON) with regions susceptible to sidewall IA formation whilst haemodynamic indices associated with the oscillation of the WSS vectors have much lower correlations.

Intra-aneurysmal pressure and flow changes induced by flow diverters: relation to aneurysm size and shape

BACKGROUND AND PURPOSE

Effects of blood flow modification by flow diverters are observed to lead often to aneurysm thrombosis and reverse remodeling. For this process, to further understand the potential roles of intra-aneurysmal blood pressure changes and aneurysm morphologies, 23 patients were studied by numeric simulation.

MATERIALS AND METHODS

3D imaging of aneurysms of different sizes and shapes, all located at the supraclinoid segment of the ICA (n=23), was prepared for CFD simulations. Hemodynamic variables were calculated for conditions before and after virtual FD implantation, reconstituting a vessel wall scaffold across the aneurysm neck. WSS, velocity, residence time, turnover time, and intra-aneurysmal pressure were assessed statistically.

RESULTS

After placement of FDs, significant reductions inside the aneurysm were observed for most hemodynamic variables (P<.01) except mean intra-aneurysmal pressures. For minimum/maximum intra-aneurysmal pressure values, small but significant changes were found; however, they were considered too small to be of relevance.

CONCLUSIONS

Calculations in 23 cases did not reveal significant intra-aneurysmal mean or peak pressure changes, indicating a minor role of pressure changes in the rare event of secondary ruptures after FD use. Other hemodynamic variables (WSS and velocity) exhibited more significant changes, indicating their role in intra-aneurysmal thrombus formation. Size-dependent, significantly higher reduction in WSS (P=.069) and velocity (P=.013) was observed in small aneurysms compared with larger ones. When it came to shape, there were significantly higher reductions in WSS (P=.055) and velocity (P=.065) and a significantly higher increase in turnover time in fusiform aneurysms compared with saccular aneurysms.

Accuracy vs. computational time: translating aortic simulations to the clinic

State of the art simulations of aortic haemodynamics feature full fluid-structure interaction (FSI) and coupled 0D boundary conditions. Such analyses require not only significant computational resource but also weeks to months of run time, which compromises the effectiveness of their translation to a clinical workflow. This article employs three computational fluid methodologies, of varying levels of complexity with coupled 0D boundary conditions, to simulate the haemodynamics within a patient-specific aorta. The most comprehensive model is a full FSI simulation. The simplest is a rigid walled incompressible fluid simulation while an alternative middle-ground approach employs a compressible fluid, tuned to elicit a response analogous to the compliance of the aortic wall. The results demonstrate that, in the context of certain clinical questions, the simpler analysis methods may capture the important characteristics of the flow field.

The Virtual Physiological Human

The Virtual Physiological Human is synonymous with a programme in computational biomedicine that aims to develop a framework of methods and technologies to investigate the human body as a whole. It is predicated on the transformational character of information technology, brought to bear on that most crucial of human concerns, our own health and well-being.