Samuel Voß

Does the DSA reconstruction kernel affect hemodynamic predictions in intracranial aneurysms? An analysis of geometry and blood flow variations

Background Computational fluid dynamics (CFD) blood flow predictions in intracranial aneurysms promise great potential to reveal patient-specific flow structures. Since the workflow from image acquisition to the final result includes various processing steps, quantifications of the individual introduced potential error sources are required. Methods Three-dimensional (3D) reconstruction of the acquired imaging data as input to 3D model generation was evaluated. Six different reconstruction modes for 3D digital subtraction angiography (DSA) acquisitions were applied to eight patient-specific aneurysms. Segmentations were extracted to compare the 3D luminal surfaces. Time-dependent CFD simulations were carried out in all 48 configurations to assess the velocity and wall shear stress (WSS) variability due to the choice of reconstruction kernel. Results All kernels yielded good segmentation agreement in the parent artery; deviations of the luminal surface were present at the aneurysm neck (up to 34.18%) and in distal or perforating arteries. Observations included pseudostenoses as well as noisy surfaces, depending on the selected reconstruction kernel. Consequently, the hemodynamic predictions show a mean SD of 11.09% for the aneurysm neck inflow rate, 5.07% for the centerline-based velocity magnitude, and 17.83%/9.53% for the mean/max aneurysmal WSS, respectively. In particular, vessel sections distal to the aneurysms yielded stronger variations of the CFD values. Conclusions The choice of reconstruction kernel for DSA data influences the segmentation result, especially for small arteries. Therefore, if precise morphology measurements or blood flow descriptions are desired, a specific reconstruction setting is required. Furthermore, research groups should be encouraged to denominate the kernel types used in future hemodynamic studies.

Fluid-Structure Simulations of a Ruptured Intracranial Aneurysm: Constant versus Patient-Specific Wall Thickness.

Computational Fluid Dynamics is intensively used to deepen the understanding of aneurysm growth and rupture in order to support physicians during therapy planning. However, numerous studies considering only the hemodynamics within the vessel lumen found no satisfactory criteria for rupture risk assessment. To improve available simulation models, the rigid vessel wall assumption has been discarded in this work and patient-specific wall thickness is considered within the simulation. For this purpose, a ruptured intracranial aneurysm was prepared ex vivo, followed by the acquisition of local wall thickness using μCT. The segmented inner and outer vessel surfaces served as solid domain for the fluid-structure interaction (FSI) simulation. To compare wall stress distributions within the aneurysm wall and at the rupture site, FSI computations are repeated in a virtual model using a constant wall thickness approach. Although the wall stresses obtained by the two approaches—when averaged over the complete aneurysm sac—are in very good agreement, strong differences occur in their distribution. Accounting for the real wall thickness distribution, the rupture site exhibits much higher stress values compared to the configuration with constant wall thickness. The study reveals the importance of geometry reconstruction and accurate description of wall thickness in FSI simulations.

Glyph-Based Comparative Stress Tensor Visualization in Cerebral Aneurysms

We present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient‐specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture‐prone criterion. We use different glyph‐based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk. We developed a GPU‐based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data: Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data

Intravascular imaging provides new insights into the condition of vessel walls. This is crucial for cerebrovascular diseases including stroke and cerebral aneurysms, where it may present an important factor for indication of therapy. In this work, we provide new information of cerebral artery walls by combining ex vivo optical coherence tomography (OCT) imaging with histology data sets. To overcome the obstacles of deflated and collapsed vessels due to the missing blood pressure, the lack of co‐alignment as well as the geometrical shape deformations due to catheter probing, we developed the new image processing method virtual inflation. We locally sample the vessel wall thickness based on the (deflated) vessel lumen border instead of the vessels centerline. Our method is embedded in a multi‐view framework where correspondences between OCT and histology can be highlighted via brushing and linking yielding OCT signal characteristics of the cerebral artery wall and its pathologies. Finally, we enrich the data views with a hierarchical clustering representation which is linked via virtual inflation and further supports the deduction of vessel wall pathologies.

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data.

Intravascular imaging provides new insights into the condition of vessel walls. This is crucial for cerebrovascular diseases including stroke and cerebral aneurysms, where it may present an important factor for indication of therapy. In this work, we provide new information of cerebral artery walls by combining ex vivo optical coherence tomography (OCT) imaging with histology data sets. To overcome the obstacles of deflated and collapsed vessels due to the missing blood pressure, the lack of co‐alignment as well as the geometrical shape deformations due to catheter probing, we developed the new image processing method virtual inflation. We locally sample the vessel wall thickness based on the (deflated) vessel lumen border instead of the vessels centerline. Our method is embedded in a multi‐view framework where correspondences between OCT and histology can be highlighted via brushing and linking yielding OCT signal characteristics of the cerebral artery wall and its pathologies. Finally, we enrich the data views with a hierarchical clustering representation which is linked via virtual inflation and further supports the deduction of vessel wall pathologies.

Bringing hemodynamic simulations closer to the clinics: A CFD prototype study for intracranial aneurysms

Computational Fluid Dynamics enables the investigation of patient-specific hemodynamics for rupture predictions and treatment support of intracranial aneurysms. However, due to numerous simplifications to decrease the computations effort, clinical applicability is limited until now. To overcome this situation a clinical research software prototype was tested that can be easily operated by attending physicians. In order to evaluate the accuracy of this prototype, four patient-specific intracranial aneurysms were investigated using four different spatial resolutions. The results demonstrate that physicians were able to generate hemodynamic predictions within several minutes at low spatial resolution. However, depending on the parameter of interest and the desired accuracy, higher resolutions are required, which will lead to an increase of computational times that still look very attractive towards clinical usability. The study shows that the next step towards an applicable individualized therapy for patients harboring intracranial aneurysms can be done. However, further in vivo validations are required to guarantee realistic predictions in future studies.

From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms

Abstract Computational fluid dynamics (CFD) is increasingly used by biomedical engineering groups to understand and predict the blood flow within intracranial aneurysms and support the physician during therapy planning. However, due to various simplifications, its acceptance remains limited within the medical community. To quantify the influence of the reconstruction kernels employed for reconstructing 3D images from rotational angiography data, different kernels are applied to four datasets with patient-specific intracranial aneurysms. Sharp, normal and smooth reconstructions were evaluated. Differences of the resulting 24 segmentations and the impact on the hemodynamic predictions are quantified to provide insights into the expected error ranges. A comparison of the segmentations yields strong differences regarding vessel branches and diameters. Further, sharp kernels lead to smaller ostium areas than smooth ones. Analyses of hemodynamic predictions reveal a clear time and space dependency, while mean velocity deviations range from 3.9 to 8%. The results reveal a strong influence of reconstruction kernels on geometrical aneurysm models and the subsequent hemodynamic parameters. Thus, patient-specific blood flow predictions require a carefully selected reconstruction kernel and appropriate recommendations need to be formulated.

Exploration of blood flow patterns in cerebral aneurysms during the cardiac cycle

Abstract This paper presents a method for clustering time-dependent blood flow data, represented by path lines, in cerebral aneurysms using a reliable similarity measure combined with a clustering technique. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of investigating aberrant blood flow patterns for the patient-specific rupture risk assessment and treatment analysis. Therefore, occurring flow patterns are manually extracted and classified according to predefined criteria. The manual extraction is time-consuming for larger studies and affected by visual clutter, which complicates the subsequent classification of flow patterns. In contrast, our method allows an automatic and reliable clustering of intra-aneurysmal flow patterns that facilitates their classification. We introduce a similarity measure that groups spatio-temporally adjacent flow patterns. We combine our similarity measure with a commonly used clustering technique and applied it to five representative datasets. The clustering results are presented by 2D and 3D visualizations and were qualitatively compared and evaluated by four domain experts. Moreover, we qualitatively evaluated our similarity measure.

Fluid-structure interaction in intracranial vessel walls: The role of patient-specific wall thickness

Abstract Computational Fluid Dynamics studies try to support physicians during therapy planning of intracranial aneurysms. However, multiple assumptions (e.g. rigid vessel walls) are required leading to a sparse acceptance of numerical approaches within the medical community. This study incorporates multiple fluid-structural simulations for an intracranial basilar artery bifurcation. Based on a patient-specific dataset, which was acquired using optical coherence tomography, minimum, mean, maximum, and diameter-dependent thicknesses were generated and compared w.r.t. hemodynamic and wall stress parameters. The comparison of different wall thickness models revealed a strong variability among the analyzed parameters depending on the corresponding assumption. Using the patient-specific configuration as a reference, constant thicknesses lead to differences of up to 100 % in the mean wall stresses. Even the diameter-dependent thickness results in deviations of 32 %, demonstrating the wide variability of computational predictions due to inaccurate assumptions. The findings of this study highlight the importance of geometry reconstruction including accurate wall thickness reproduction for fluid-structure simulations. Patient-specific wall thickness seems to be out of alternatives regarding the realistic prediction of wall stress distributions.

Impact of Gradual Vascular Deformations on the Intra-aneurysmal Hemodynamics

The treatment of intracranial aneurysms based on stentassisted coiling often leads to local vascular deformations. Patient-specific data of an aneurysm in the pre interventional and follow-up state is used to interpolate intermediate vessel-aneurysm configurations. Computational Fluid Dynamics simulations are performed in order to quantify the effect of vessel deformation on the blood flow. Results reveal gradual changes in the blood flow patterns shifting the load on the aneurysm wall from the dome to the neck region. Based on this novel concept, it is possible to virtually evaluate how different types of stents can improve or impair the treatment goal of reducing the intra-aneurysmal blood flow.