Sylvia Saalfeld1,2, Philipp Berg3,4, Annika Niemann5, Maria Luz5, Bernhard Preim5,4, Oliver Beuing6,4. 1. Department of Simulation and Graphics, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany. sylvia.saalfeld@ovgu.de. 2. Research Campus STIMULATE, Magdeburg, Germany. sylvia.saalfeld@ovgu.de. 3. Department of Fluid Dynamics and Technical Flows, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany. 4. Research Campus STIMULATE, Magdeburg, Germany. 5. Department of Simulation and Graphics, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany. 6. Department of Neuroradiology, University Hospital of Magdeburg, Magdeburg, Germany.
Abstract
PURPOSE: Morphological parameters of intracranial aneurysms (IAs) are well established for rupture risk assessment. However, a manual measurement is error-prone, not reproducible and cumbersome. For an automatic extraction of morphological parameters, a 3D neck curve reconstruction approach to delineate the aneurysm from the parent vessel is required. METHODS: We present a 3D semiautomatic aneurysm neck curve reconstruction for the automatic extraction of morphological parameters which was developed and evaluated with an experienced neuroradiologist. We calculate common parameters from the literature and include two novel angle-based parameters: the characteristic dome point angle and the angle difference of base points. RESULTS: We applied our method to 100 IAs acquired with rotational angiography in clinical routine. For validation, we compared our approach to manual segmentations yielding highly significant correlations. We analyzed 95 of these datasets regarding rupture state. Statistically significant differences were found in ruptured and unruptured groups for maximum diameter, maximum height, aspect ratio and the characteristic dome point angle. These parameters were also found to statistically significantly correlate with each other. CONCLUSIONS: The new 3D neck curve reconstruction provides robust results for all datasets. The reproducibility depends on the vessel tree centerline and the user input for the initial dome point and parameters characterizing the aneurysm neck region. The characteristic dome point angle as a new metric regarding rupture risk assessment can be extracted. It requires less computational effort than the complete neck curve reconstruction.
PURPOSE: Morphological parameters of intracranial aneurysms (IAs) are well established for rupture risk assessment. However, a manual measurement is error-prone, not reproducible and cumbersome. For an automatic extraction of morphological parameters, a 3D neck curve reconstruction approach to delineate the aneurysm from the parent vessel is required. METHODS: We present a 3D semiautomatic aneurysm neck curve reconstruction for the automatic extraction of morphological parameters which was developed and evaluated with an experienced neuroradiologist. We calculate common parameters from the literature and include two novel angle-based parameters: the characteristic dome point angle and the angle difference of base points. RESULTS: We applied our method to 100 IAs acquired with rotational angiography in clinical routine. For validation, we compared our approach to manual segmentations yielding highly significant correlations. We analyzed 95 of these datasets regarding rupture state. Statistically significant differences were found in ruptured and unruptured groups for maximum diameter, maximum height, aspect ratio and the characteristic dome point angle. These parameters were also found to statistically significantly correlate with each other. CONCLUSIONS: The new 3D neck curve reconstruction provides robust results for all datasets. The reproducibility depends on the vessel tree centerline and the user input for the initial dome point and parameters characterizing the aneurysm neck region. The characteristic dome point angle as a new metric regarding rupture risk assessment can be extracted. It requires less computational effort than the complete neck curve reconstruction.
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Authors: Naomi Larsen; Charlotte Flüh; Sylvia Saalfeld; Samuel Voß; Georg Hille; David Trick; Fritz Wodarg; Michael Synowitz; Olav Jansen; Philipp Berg Journal: Neuroradiology Date: 2020-07-17 Impact factor: 2.804
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Authors: Vanessa M Swiatek; Belal Neyazi; Jorge A Roa; Mario Zanaty; Edgar A Samaniego; Daizo Ishii; Yongjun Lu; I Erol Sandalcioglu; Sylvia Saalfeld; Philipp Berg; David M Hasan Journal: Neurosurgery Date: 2021-09-15 Impact factor: 5.315