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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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Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study
Abstract BackgroundIntraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD‐based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. PurposeBuilding on the identified limitations in QA, the study aims to adapt SVD‐based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two‐dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics. Materials and methodsThe study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time‐density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block‐circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient‐specific internal carotid artery phantom. The DSA showcases factors like x‐ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data. ResultsThe different SVD algorithm variants showed strong correlations between flow and deconvolution‐adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. ConclusionImplementing SVD‐based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.
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- Award ID(s):
- 2304388
- PAR ID:
- 10537101
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Medical Physics
- ISSN:
- 0094-2405
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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