Leveraging patient-specific simulated angiograms to characterize cerebral aneurysm hemodynamics using computational fluid dynamics

Chivukula, Venkat Keshav; White, Ruth Moore; Shields, Allison; Davies, Jason M.; Mokin, Maxim; Bednarek, Daniel R.; Rudin, Stephen; Ionita, Ciprian N.

doi:10.1117/12.2611473

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…Additionally, further exploration of the effect of the contrast injection parameters on image-derived biomarkers is warranted. It should be noted that additional methodologies have been developed to simulate the physical interaction between both the blood and contrast materials 18 : because our focus was to generate a 3D ground-truth contrast flow object, thus enabling a deeper exploration of foreshortening effects and the development of an appropriate correction schema, the passive scalar approach provided the optimal framework to complete this investigation. The use of an automated contrast injection at an appropriate distance from the aneurysmal ROI ideally will not perturb the underlying hemodynamics, although this a general limitation of all quantitative angiographic methods.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed framework allows for the generation of a ground-truth volumetric contrast distribution in any vessel of interest. 18,19 Using the simulated acquisition geometry and known contrast conditions, a pathlengthcorrection metric is investigated to further reduce the effects of depth integration, vessel overlap, and foreshortening on conventional 2D-API analysis, improving the interpretability of these angiography-derived biomarkers. We performed voxel-wise (3D) API on the ground-truth contrast distribution and compared it to pixel-wise (2D) API, both with and without pathlength correction.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing cerebral vasculature analysis with pathlength‐corrected 2D angiographic parametric imaging: A feasibility study

Shields,

Williams,

Bhurwani

et al. 2023

Medical Physics

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Background2D angiographic parametric imaging (API) quantitatively extracts imaging biomarkers related to contrast flow and is conventionally applied to 2D digitally subtracted angiograms (DSA's). In the interventional suite, API is typically performed using 1–2 projection views and is limited by vessel overlap, foreshortening, and depth‐integration of contrast motion.PurposeThis work explores the use of a pathlength‐correction metric to overcome the limitations of 2D‐API: the primary objective was to study the effect of converting 3D contrast flow to projected contrast flow using a simulated angiographic framework created with computational fluid dynamics (CFD) simulations, thereby removing acquisition variability.MethodsThe pathlength‐correction framework was applied to in‐silico angiograms, generating a reference (i.e., ground‐truth) volumetric contrast distribution in four patient‐specific intracranial aneurysm geometries. Biplane projections of contrast flow were created from the reference volumetric contrast distributions, assuming a cone‐beam geometry. A Parker‐weighted reconstruction was performed to obtain a binary representation of the vessel structure in 3D. Standard ray tracing techniques were then used to track the intersection of a ray from the focal spot with each voxel of the reconstructed vessel wall to a pixel in the detector plane. The lengths of each ray through the 3D vessel lumen were then projected along each ray‐path to create a pathlength‐correction map, where the pixel intensity in the detector plane corresponds to the vessel width along each source‐detector ray. By dividing the projection sequences with this correction map, 2D pathlength‐corrected in‐silico angiograms were obtained. We then performed voxel‐wise (3D) API on the ground‐truth contrast distribution and compared it to pixel‐wise (2D) API, both with and without pathlength correction for each biplane view. The percentage difference (PD) between the resultant API biomarkers in each dataset were calculated within the aneurysm region of interest (ROI).ResultsIntensity‐based API parameters, such as the area under the curve (AUC) and peak height (PH), exhibited notable changes in magnitude and spatial distribution following pathlength correction: these now accurately represent conservation of mass of injected contrast media within each arterial geometry and accurately reflect regions of stagnation and recirculation in each aneurysm ROI. Improved agreement was observed between these biomarkers in the pathlength‐corrected biplane maps: the maximum PD within the aneurysm ROI is 3.3% with pathlength correction and 47.7% without pathlength correction. As expected, improved agreement with ROI‐averaged ground‐truth 3D counterparts was observed for all aneurysm geometries, particularly large aneurysms: the maximum PD for both AUC and PH was 5.8%. Temporal parameters (mean transit time, MTT, time‐to‐peak, TTP, time‐to‐arrival, TTA) remained unaffected after pathlength correction.ConclusionsThis study indicates that the values of intensity‐based API parameters obtained with conventional 2D‐API, without pathlength correction, are highly dependent on the projection orientation, and uncorrected API should be avoided for hemodynamic analysis. The proposed metric can standardize 2D API‐derived biomarkers independent of projection orientation, potentially improving the diagnostic value of all acquired 2D‐DSA's. Integration of a pathlength correction map into the imaging process can allow for improved interpretation of biomarkers in 2D space, which may lead to improved diagnostic accuracy during procedures involving the cerebral vasculature.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing cerebral vasculature analysis with pathlength‐corrected 2D angiographic parametric imaging: A feasibility study

Shields,

Williams,

Bhurwani

et al. 2023

Medical Physics

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“…High spatial and temporal resolution transient laminar flow models were created using a custom two-fluid SA approach in ANSYS FLUENT (Ansys Inc., Canonsburg, PA) 14 . The two interacting fluids (blood and contrast agent) were modeled as Newtonian fluids with each having independent mechanical properties.…”

Section: Contrast Flow Simulationsmentioning

confidence: 99%

“…The central hypothesis of this work is that contrast agent transport is representative of the underlying hemodynamics, and as such, the flow of contrast agent can provide quantitative hemodynamic insight. We use a CFD-based virtual angiographic injection methodology to create simulated angiograms (SA) in several patient-specific geometries to analyze the correlation between contrast agent transport and the underlying hemodynamics [14][15][16][17] . Specifically, we quantify the influence of contrast injection parameters such as injection duration, and anatomical features such as aneurysmal morphology on the contrast agent transport.…”

Section: Introductionmentioning

confidence: 99%

Investigating angiographic injection parameters for cerebral aneurysm hemodynamic characterization using patient-specific simulated angiograms

White

Shields

Nagesh

et al. 2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Cerebral aneurysm (CA) rupture is one of the major causes of hemorrhagic stroke. During endovascular therapy (ET), neurointerventionalists rely on qualitative image sequences and do not have access to crucial quantitative hemodynamic information. Quantifying angiographic image sequences can provide vital information, but it is not possible to perform this in a controlled manner in vivo. Computational fluid dynamics (CFD) is a valuable tool capable of providing high fidelity quantitative data by replicating the blood flow physics within the cerebrovasculature. In this work, we use simulated angiograms (SA) to quantify the hemodynamic interaction with a clinically utilized contrast agent. SA enables extraction of time density curves (TDC) within the desired region of interest to analyze hemodynamic parameters such as time to peak (TTP) and mean transit time (MTT) within the aneurysm. We present on the quantification of several hemodynamic parameters of interest for multiple, clinically-relevant scenarios such as variable contrast injection duration and bolus volumes for 7 patient-specific CA geometries. Results indicate that utilizing these analyses provides valuable hemodynamic information relating vascular and aneurysm morphology, contrast flow conditions and injection variability. The injected contrast circulates for multiple cardiac cycles within the aneurysmal region, especially for larger aneurysms and tortuous vasculature. The SA approach enables determination of angiographic parameters for each scenario. Together, these have the potential to overcome the existing barriers in quantifying angiographic procedures in vitro or in vivo, and can provide clinically valuable hemodynamic insights for CA treatment.

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“…The resulting meshes contained approximately 600k-900k elements: this mesh resolution was chosen to preserve memory resources, as all finite element data is exported at each timestep, while still recovering the dominant flow patterns within the aneurysm (major inflow jet, vortices, etc.). Each mesh was imported into ANSYS FLUENT (ANSYS Inc., Canonsburg, PA), where time-resolved contrast distributions were generated using a two-fluid approach [16]. This method resolves the physics between the interaction of the contrast media with that of the surrounding fluid (blood), where each material is defined as a Newtonian fluid with its own density and viscosity (Table 1).…”

Section: Computational Fluid Dynamics Simulationmentioning

confidence: 99%

2D versus 3D comparison of angiographic imaging biomarkers using computational fluid dynamics simulations of contrast injections

Shields

Bhurwani²,

Williams

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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Quantitative angiography (QAngio) may provide hemodynamic information during neurointerventional procedures through imaging biomarkers related to contrast flow. The standard clinical implementation of QAngio is limited by projection imaging: analysis of contrast motion within complex 3D geometries is restricted to 1-2 projection views, truncating the potential wealth of imaging biomarkers related to disease progression or efficacy of treatment. To understand the limitations of 2D biomarkers, we propose the use of in-silico contrast distributions to investigate the potential benefits of 3D-QAngio within the context of neurovascular hemodynamics. Ground-truth in-silico contrast distributions were generated in two patient-specific intracranial aneurysm models, accounting for the physical interactions of contrast media and blood. A short bolus of contrast was utilized to obtain full a wash-in/ wash-out cycle within the aneurysm ROI. Simulated angiograms mimicking clinical cone-beam CT (CBCT) acquisitions were then generated, and volumetric contrast distributions were reconstructed to analyze bulk contrast flow. The ground-truth 3D-CFD, reconstructed 3D-CBCT-DSA, and 2D-DSA projections were used to extract QAngio parameters related to contrast time dilution curves, such as area under the curve (AUC), peak height (PH), mean-transit-time (MTT), time-to-peak (TTP), and time to arrival (TTA). An initial comparison of quantitative flow parameters in both 2D and 3D, in a smaller and larger aneurysm, indicated that 3D-QAngio can provide a good description of bulk flow characteristics (TTA, TTP, MTT), but recovery of integral parameters (PH, AUC) aneurysms is limited. Nonetheless, incorporation of 3D-QAngio methods may provide additional insight into our understanding of abnormal vascular flow patterns.

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Leveraging patient-specific simulated angiograms to characterize cerebral aneurysm hemodynamics using computational fluid dynamics

Cited by 3 publications

References 20 publications

Enhancing cerebral vasculature analysis with pathlength‐corrected 2D angiographic parametric imaging: A feasibility study

Enhancing cerebral vasculature analysis with pathlength‐corrected 2D angiographic parametric imaging: A feasibility study

Investigating angiographic injection parameters for cerebral aneurysm hemodynamic characterization using patient-specific simulated angiograms

2D versus 3D comparison of angiographic imaging biomarkers using computational fluid dynamics simulations of contrast injections

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