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.
- Blood Flow
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Contributors Conception and design: PB, SS. Acquisition of data: OB, PB, SS, SV. Analysis and interpretation of data: PB, SS, SV, OB, TR. Drafting the article: PB. Critically revising the article: SS, PB, TR, GJ, BP. Statistical analysis: PB, SS, SV. Administrative/technical/material support: TR, GJ, BP. Study supervision: OB, GJ, BP. Guarantor: PB.
Funding This work was supported by the Federal Ministry of Education and Research in Germany under grant number 13GW0095A.
Disclaimer The concepts and information presented in this paper are based on research and are not commercially available.
Competing interests TR is employee of Siemens Healthcare GmbH.
Patient consent Obtained.
Ethics approval Ethics approval was obtained from University Hospital Magdeburg.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Datasets and case-specific geometry and blood flow information are available upon request from the corresponding author.