P Berg1, S Saalfeld2, S Voß1, T Redel3, B Preim2, G Janiga1, O Beuing4. 1. Department of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany. 2. Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany. 3. Siemens Healthcare GmbH, Forchheim, Germany. 4. Institute of Neuroradiology, University Hospital Magdeburg, Magdeburg, Germany.
Abstract
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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
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Authors: Kristian Valen-Sendstad; Aslak W Bergersen; Yuji Shimogonya; Leonid Goubergrits; Jan Bruening; Jordi Pallares; Salvatore Cito; Senol Piskin; Kerem Pekkan; Arjan J Geers; Ignacio Larrabide; Saikiran Rapaka; Viorel Mihalef; Wenyu Fu; Aike Qiao; Kartik Jain; Sabine Roller; Kent-Andre Mardal; Ramji Kamakoti; Thomas Spirka; Neil Ashton; Alistair Revell; Nicolas Aristokleous; J Graeme Houston; Masanori Tsuji; Fujimaro Ishida; Prahlad G Menon; Leonard D Browne; Stephen Broderick; Masaaki Shojima; Satoshi Koizumi; Michael Barbour; Alberto Aliseda; Hernán G Morales; Thierry Lefèvre; Simona Hodis; Yahia M Al-Smadi; Justin S Tran; Alison L Marsden; Sreeja Vaippummadhom; G Albert Einstein; Alistair G Brown; Kristian Debus; Kuniyasu Niizuma; Sherif Rashad; Shin-Ichiro Sugiyama; M Owais Khan; Adam R Updegrove; Shawn C Shadden; Bart M W Cornelissen; Charles B L M Majoie; Philipp Berg; Sylvia Saalfield; Kenichi Kono; David A Steinman Journal: Cardiovasc Eng Technol Date: 2018-09-10 Impact factor: 2.495