Computational fluid dynamics‐based virtual angiograms for the detection of flow stagnation in intracranial aneurysms

The goal of this study was to test if CFD‐based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computi...

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Published inInternational journal for numerical methods in biomedical engineering Vol. 39; no. 8; pp. e3740 - n/a
Main Authors Hadad, Sara, Karnam, Yogesh, Mut, Fernando, Lohner, Rainald, Robertson, Anne M., Kaneko, Naoki, Cebral, Juan R.
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.08.2023
Wiley Subscription Services, Inc
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ISSN2040-7939
2040-7947
2040-7947
DOI10.1002/cnm.3740

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Abstract The goal of this study was to test if CFD‐based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject‐specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD‐based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD‐based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents. CFD‐based virtual angiograms can accurately detect flow stagnation in IAs and identify aneurysms with stagnation with over 90% accuracy, even without considering gravitational effects on contrast agents.
AbstractList The goal of this study was to test if CFD-based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject-specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD-based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD-based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents.The goal of this study was to test if CFD-based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject-specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD-based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD-based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents.
The goal of this study was to test if CFD‐based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject‐specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD‐based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD‐based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents. CFD‐based virtual angiograms can accurately detect flow stagnation in IAs and identify aneurysms with stagnation with over 90% accuracy, even without considering gravitational effects on contrast agents.
The goal of this study was to test if CFD‐based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject‐specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD‐based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD‐based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents.
Author Mut, Fernando
Kaneko, Naoki
Karnam, Yogesh
Robertson, Anne M.
Cebral, Juan R.
Hadad, Sara
Lohner, Rainald
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flow stagnation
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Snippet The goal of this study was to test if CFD‐based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and...
The goal of this study was to test if CFD-based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and...
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StartPage e3740
SubjectTerms Aneurysm
Aneurysms
Angiography
Angiography, Digital Subtraction
Arteries
Blood flow
CFD
Computational fluid dynamics
Computer applications
Contrast agents
Contrast Media
Digital imaging
flow stagnation
Fluid dynamics
Gravitational effects
Gravity
Gravity effects
Hemodynamics
Humans
Hydrodynamics
Imaging, Three-Dimensional
Injection
Intracranial Aneurysm - diagnostic imaging
Mathematical models
Medical imaging
pooling
Retention
Retention time
Simulation
Stagnation
Three dimensional models
Transport equations
virtual angiogram
Title Computational fluid dynamics‐based virtual angiograms for the detection of flow stagnation in intracranial aneurysms
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcnm.3740
https://www.ncbi.nlm.nih.gov/pubmed/37288602
https://www.proquest.com/docview/2845027850
https://www.proquest.com/docview/2823988367
Volume 39
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