Nicole Varble

Challenges and limitations of patient-specific vascular phantom fabrication using 3D Polyjet printing

Additive manufacturing (3D printing) technology offers a great opportunity towards development of patient-specific vascular anatomic models, for medical device testing and physiological condition evaluation. However, the development process is not yet well established and there are various limitations depending on the printing materials, the technology and the printer resolution. Patient-specific neuro-vascular anatomy was acquired from computed tomography angiography and rotational digital subtraction angiography (DSA). The volumes were imported into a Vitrea 3D workstation (Vital Images Inc.) and the vascular lumen of various vessels and pathologies were segmented using a “marching cubes” algorithm. The results were exported as Stereo Lithographic (STL) files and were further processed by smoothing, trimming, and wall extrusion (to add a custom wall to the model). The models were printed using a Polyjet printer, Eden 260V (Objet-Stratasys). To verify the phantom geometry accuracy, the phantom was reimaged using rotational DSA, and the new data was compared with the initial patient data. The most challenging part of the phantom manufacturing was removal of support material. This aspect could be a serious hurdle in building very tortuous phantoms or small vessels. The accuracy of the printed models was very good: distance analysis showed average differences of 120 μm between the patient and the phantom reconstructed volume dimensions. Most errors were due to residual support material left in the lumen of the phantom. Despite the post-printing challenges experienced during the support cleaning, this technology could be a tremendous benefit to medical research such as in device development and testing.

Flow Instability Detected by High-Resolution Computational Fluid Dynamics in Fifty-Six Middle Cerebral Artery Aneurysms

Recent high-resolution computational fluid dynamics (CFD) studies have detected persistent flow instability in intracranial aneurysms (IAs) that was not observed in previous in silico studies. These flow fluctuations have shown incidental association with rupture in a small aneurysm dataset. The aims of this study are to explore the capabilities and limitations of a commercial cfd solver in capturing such velocity fluctuations, whether fluctuation kinetic energy (fKE) as a marker to quantify such instability could be a potential parameter to predict aneurysm rupture, and what geometric parameters might be associated with such fluctuations. First, we confirmed that the second-order discretization schemes and high spatial and temporal resolutions are required to capture these aneurysmal flow fluctuations. Next, we analyzed 56 patient-specific middle cerebral artery (MCA) aneurysms (12 ruptured) by transient, high-resolution CFD simulations with a cycle-averaged, constant inflow boundary condition. Finally, to explore the mechanism by which such flow instabilities might arise, we investigated correlations between fKE and several aneurysm geometrical parameters. Our results show that flow instabilities were present in 8 of 56 MCA aneurysms, all of which were unruptured bifurcation aneurysms. Statistical analysis revealed that fKE could not differentiate ruptured from unruptured aneurysms. Thus, our study does not lend support to these flow instabilities (based on a cycle-averaged constant inflow as opposed to peak velocity) being a marker for rupture. We found a positive correlation between fKE and aneurysm size as well as size ratio. This suggests that the intrinsic flow instability may be associated with the breakdown of an inflow jet penetrating the aneurysm space.

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.

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms

Background and Purpose— Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture. Methods— We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs). Results— Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78–0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82–0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61–0.88 versus 0.67; 95% confidence interval, 0.52–0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model. Conclusions— Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

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.

Identification of vortex structures in a cohort of 204 intracranial aneurysms

An intracranial aneurysm (IA) is a cerebrovascular pathology that can lead to death or disability if ruptured. Abnormal wall shear stress (WSS) has been associated with IA growth and rupture, but little is known about the underlying flow physics related to rupture-prone IAs. Previous studies, based on analysis of a few aneurysms or partial views of three-dimensional vortex structures, suggest that rupture is associated with complex vortical flow inside IAs. To further elucidate the relevance of vortical flow in aneurysm pathophysiology, we studied 204 patient IAs (56 ruptured and 148 unruptured). Using objective quantities to identify three-dimensional vortex structures, we investigated the characteristics associated with aneurysm rupture and if these features correlate with previously proposed WSS and morphological characteristics indicative of IA rupture. Based on the Q-criterion definition of a vortex, we quantified the degree of the aneurysmal region occupied by vortex structures using the volume vortex fraction (vVF) and the surface vortex fraction (sVF). Computational fluid dynamics simulations showed that the sVF, but not the vVF, discriminated ruptured from unruptured aneurysms. Furthermore, we found that the near-wall vortex structures co-localized with regions of inflow jet breakdown, and significantly correlated to previously proposed haemodynamic and morphologic characteristics of ruptured IAs.

Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms

OBJECTIVE Precise morphologic evaluation is important for intracranial aneurysm (IA) management. At present, clinicians manually measure the IA size and neck diameter on 2-dimensional (2D) digital subtraction angiographic (DSA) images and categorize the IA shape as regular or irregular on 3-dimensional (3D)-DSA images, which could result in inconsistency and bias. We investigated whether a computer-assisted 3D analytical approach could improve IA morphology assessment. METHODS Five neurointerventionists evaluated the size, neck diameter, and shape of 39 IAs using current and computer-assisted 3D approaches. In the computer-assisted 3D approach, the size, neck diameter, and undulation index (UI, a shape irregularity metric) were extracted using semiautomated reconstruction of aneurysm geometry using 3D-DSA, followed by IA neck identification and computerized geometry assessment. RESULTS The size and neck diameter measured using the manual 2D approach were smaller than computer-assisted 3D measurements by 2.01 mm (P < 0.001) and 1.85 mm (P < 0.001), respectively. Applying the definitions of small IAs (<7 mm) and narrow-necked IAs (<4 mm) from the reported data, interrater variation in manual 2D measurements resulted in inconsistent classification of the size of 14 IAs and the necks of 19 IAs. Visual inspection resulted in an inconsistent shape classification for 23 IAs among the raters. Greater consistency was achieved using the computer-assisted 3D approach for size (intraclass correlation coefficient [ICC], 1.00), neck measurements (ICC, 0.96), and shape quantification (UI; ICC, 0.94). CONCLUSIONS Computer-assisted 3D morphology analysis can improve accuracy and consistency in measurements compared with manual 2D measurements. It can also more reliably quantify shape irregularity using the UI. Future application of computer-assisted analysis tools could help clinicians standardize morphology evaluations, leading to more consistent IA evaluations.

Vortex Dynamics of Ruptured and Unruptured Intracranial Aneurysms

Intracranial aneurysms are a potentially devastating pathological dilation of brain arteries that affect 1–5 % of the population. In this study we investigated the vortex structures of both unruptured and ruptured intracranial aneurysms as a discriminating property. We performed pulsatile computational fluid dynamic simulations on 204 patient-specific aneurysm models (57 ruptured and 147 unruptured) derived from patient angiographic imaging. Using Q-criterion we analyzed the coherent structures both throughout the aneurysm volume and at the wall. The relative surface area with positive Q values (indicating vortices at the wall) was able to differentiate ruptured and unruptured aneurysms. For the first time, in a large patient cohort, mechanistic fluid analysis is leading to insights into rupture pathways.Copyright

AView: A Clinical Tool for Hemodynamic and Morphological Analysis of Intracranial Aneurysms

Neurointerventionists are routinely faced with the dilemma whether or not to treat unruptured intracranial aneurysms. Hemodynamic and morphological characteristics have become important considerations for aneurysm rupture-risk assessment [1]. Clinicians require an integrated tool that analyzes these parameters to help make treatment decisions in clinical workflow, however such a tool does not exist. To this end, Toshiba Stroke and Vascular Research Center (TSVRC) at University at Buffalo and Orobix Srl (Italy) have developed a prototype of a computational workflow system. Termed AView, it is an integrated, image-based vascular analysis tool for rapid assessment of aneurysmal hemodynamics, morphometrics, rupture risk assessment, and treatment planning.Copyright