TY - GEN A1 - Wagner, Thomas A1 - Krenkel, Lars A1 - Dönitz, Christian A1 - Brawanski, Alexander T1 - Influence of CFD Strategy on WSS and OSI Determination for Intracranial Aneurysm Rupture Assessment T2 - 25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria Y1 - 2019 ER - TY - JOUR A1 - Deuter, Daniel A1 - Haj, Amer A1 - Brawanski, Alexander A1 - Krenkel, Lars A1 - Schmidt, Nils Ole A1 - Doenitz, Christian T1 - Fast simulation of hemodynamics in intracranial aneurysms for clinical use JF - Acta Neurochirurgica N2 - BACKGROUND: A widely accepted tool to assess hemodynamics, one of the most important factors in aneurysm pathophysiology, is Computational Fluid Dynamics (CFD). As current workflows are still time consuming and difficult to operate, CFD is not yet a standard tool in the clinical setting. There it could provide valuable information on aneurysm treatment, especially regarding local risks of rupture, which might help to optimize the individualized strategy of neurosurgical dissection during microsurgical aneurysm clipping. METHOD: We established and validated a semi-automated workflow using 3D rotational angiographies of 24 intracranial aneurysms from patients having received aneurysm treatment at our centre. Reconstruction of vessel geometry and generation of volume meshes was performed using AMIRA 6.2.0 and ICEM 17.1. For solving ANSYS CFX was used. For validational checks, tests regarding the volumetric impact of smoothing operations, the impact of mesh sizes on the results (grid convergence), geometric mesh quality and time tests for the time needed to perform the workflow were conducted in subgroups. RESULTS: Most of the steps of the workflow were performed directly on the 3D images requiring no programming experience. The workflow led to final CFD results in a mean time of 22 min 51.4 s (95%-CI 20 min 51.562 s-24 min 51.238 s, n = 5). Volume of the geometries after pre-processing was in mean 4.46% higher than before in the analysed subgroup (95%-CI 3.43-5.50%). Regarding mesh sizes, mean relative aberrations of 2.30% (95%-CI 1.51-3.09%) were found for surface meshes and between 1.40% (95%-CI 1.07-1.72%) and 2.61% (95%-CI 1.93-3.29%) for volume meshes. Acceptable geometric mesh quality of volume meshes was found. CONCLUSIONS: We developed a semi-automated workflow for aneurysm CFD to benefit from hemodynamic data in the clinical setting. The ease of handling opens the workflow to clinicians untrained in programming. As previous studies have found that the distribution of hemodynamic parameters correlates with thin-walled aneurysm areas susceptible to rupture, these data might be beneficial for the operating neurosurgeon during aneurysm surgery, even in acute cases. Y1 - 2025 U6 - https://doi.org/10.1007/s00701-025-06469-9 VL - 167 PB - Springer ER - TY - CHAP A1 - Weber, Joachim A1 - Brawanski, Alexander A1 - Palm, Christoph T1 - Parallelization of FSL-Fast segmentation of MRI brain data T2 - 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS 2013), Lübeck, 01.-05.09.2013 Y1 - 2013 U6 - https://doi.org/10.3205/13gmds261 N1 - Meeting Abstract IS - DocAbstr. 329 PB - German Medical Science GMS Publishing House CY - Düsseldorf ER - TY - CHAP A1 - Weber, Joachim A1 - Doenitz, Christian A1 - Brawanski, Alexander A1 - Palm, Christoph T1 - Data-Parallel MRI Brain Segmentation in Clinicial Use BT - Porting FSL-Fastv4 to GPGPUs T2 - Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck N2 - Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results. KW - Brain Segmentation KW - Magnetic Resonance Imaging KW - Parallel Execution KW - Voxel Spacing KW - General Purpose Graphic Processing Unit KW - Kernspintomografie KW - Gehirn KW - Bildsegmentierung KW - Parallelverarbeitung Y1 - 2015 U6 - https://doi.org/10.1007/978-3-662-46224-9_67 SP - 389 EP - 394 PB - Springer CY - Berlin ER -