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BackgroundThe prediction of rupture in intracranial aneurysms is challenging. Aneurysm growth has been identified as a strong risk factor for rupture and aneurysm wall motion is a potential biomarker for growth, but visualizing aneurysm wall motion using conventional imaging techniques is difficult. Computational fluid dynamic simulations have been used to identify hemodynamic risk factors of intracranial aneurysm instability, but often lack observable and quantifiable biomechanical correlates that can be directly measured in vivo. MethodsIn this retrospective case–control study of matched patients, cohorts with growing (n=6) and stable (n=6) unruptured intracranial aneurysms were selected from our institutional database of 4D Flow MRI scans. The amplified Flow algorithm was used to extract maps of wall motion for each aneurysm. Hemodynamics within the aneurysm dome were calculated using established computational fluid dynamic methods, and hemodynamic variables were evaluated against wall motion for stable and growing aneurysms. ResultsSeveral hemodynamic variables were found to be both significant predictors of aneurysm growth and highly correlated with aneurysm wall motion. The hemodynamic variable most correlated with both the maximum value of aneurysm wall motion and spatial variance of aneurysm wall motion, the time coefficient of variance of the directional wall shear stress gradient (representing changing directions of wall shear stress), was also the best hemodynamic predictor of aneurysm growth. ConclusionsSpatial variance of wall motion and hemodynamic variables are increased in growing aneurysms, and the fluctuations in the directional wall shear stress correlate directly with wall motion, indicating that heterogeneous wall motion and hemodynamics are interrelated and play a critical role in aneurysm instability.
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This content will become publicly available on January 1, 2026
Modal analysis of blood flows in saccular aneurysms
Currently, it is challenging to investigate aneurismal hemodynamics based on current in vivo data such as Magnetic Resonance Imaging or Computed Tomography due to the limitations in both spatial and temporal resolutions. In this work, we investigate the use of modal analysis at various resolutions to examine its usefulness for analyzing blood flows in brain aneurysms. Two variants of Dynamic Mode Decomposition (DMD): (i) Hankel-DMD; and (ii) Optimized-DMD, are used to extract the time-dependent dynamics of blood flows during one cardiac cycle. First, high-resolution hemodynamic data in patient-specific aneurysms are obtained using Computational Fluid Dynamics. Second, the dynamics modes, along with their spatial amplitudes and temporal magnitudes are calculated using the DMD analysis. Third, an examination of DMD analyses using a range of spatial and temporal resolutions of hemodynamic data to validate the applicability of DMD for low-resolution data, similar to ones in clinical practices. Our results show that DMD is able to characterize the inflow jet dynamics by separating large-scale structures and flow instabilities even at low spatial and temporal resolutions. Its robustness in quantifying the flow dynamics using the energy spectrum is demonstrated across different resolutions in all aneurysms in our study population. Our work indicates that DMD can be used for analyzing blood flow patterns of brain aneurysms and is a promising tool to be explored in in vivo.
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- Award ID(s):
- 1946202
- PAR ID:
- 10579317
- Publisher / Repository:
- AIP Publishing
- Date Published:
- Journal Name:
- Physics of Fluids
- Volume:
- 37
- Issue:
- 1
- ISSN:
- 1070-6631
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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