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

Abstract

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, patientspecific blood flow predictions require a carefully selected reconstruction kernel and appropriate recommendations need to be formulated.

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

“…Since O’Meara et al 13 showed that the algorithm used for image reconstruction is a significant parameter, it was the focus of this study. Its importance with respect to the intra-aneurysmal flow is confirmed by the results of this study, which was motivated by findings of a preliminary work 14. These were based on 3D-DSA, reconstructed with two different kernels and three different image characteristics.…”

“…Next, a representative slice of the 3D DSA data comprising the aneurysm was selected and threshold values for the remaining reconstruction kernels were manually adapted such that the resulting isocontours matched the reference segmentation (see online supplementary table S1). Online supplementary figure 1 illustrates the segmentation process (see also Glaßer et al 14 for further details).…”

“…These studies illustrate that every single step during the post-processing needs to be addressed to reduce uncertainties and thus to increase the acceptance of CFD in the medical community. However, until now only our initial study provides an analysis of the complete workflow 14. In this study, qualitative kernel-dependent differences were presented for four patient-specific IAs.…”

Aneurysm Wall Enhancement Is Associated With Decreased Intrasaccular IL-10 and Morphological Features of Instability

“…We extracted a 3D surface model in accordance with Saalfeld et al 12,13 using 3D rotational-angiography and magnetic resonance angiography datasets. For morphological analysis, we applied a semiautomatic approach, which allows the automatic determination of 21 parameters based on the reconstruction of the neck curve 14 (Table 2).…”

A Novel Robust Topological Denoising Method Based on Homotopy Theory for Virtual Colonoscopy

“…Such surface-based colon data processing methods highly depend on the quality of the reconstructed colon surface, such that it is without topological noises or errors. With the rapid advances in data acquisition techniques in recent decades, high resolution of three-dimensional data has been produced for various applications, including real-time vision processing [10], industrial application [11], textured 3D mesh generation [12], blood flow simulation [13], cardiac structure reconstruction [14], robotic 3D reconstruction of an object [15], ceramics analysis and reconstruction [16], and unmanned-aerial-vehicles-based 3D reconstruction [17], etc. However, many existing surface reconstruction methods introduce topological noises or errors into the resulting reconstructed surfaces, which makes it intractable to perform subsequent computation tasks, such as surface simplification and parameterization.…”