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Computational fluid dynamics-based virtual angiograms for the detection of flow stagnation in intracranial aneurysms.
Hadad, Sara; Karnam, Yogesh; Mut, Fernando; Lohner, Rainald; Robertson, Anne M; Kaneko, Naoki; Cebral, Juan R.
Afiliação
  • Hadad S; Department of Bioengineering, George Mason University, Fairfax, Virginia, USA.
  • Karnam Y; Department of Bioengineering, George Mason University, Fairfax, Virginia, USA.
  • Mut F; Department of Bioengineering, George Mason University, Fairfax, Virginia, USA.
  • Lohner R; Center for Computational Fluid Dynamics, College of Science, George Mason University, Fairfax, Virginia, USA.
  • Robertson AM; Department of Mechanical Engineering and Material Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Kaneko N; Department of Interventional Neuroradiology, University of California Los Angeles, Los Angeles, California, USA.
  • Cebral JR; Department of Bioengineering, George Mason University, Fairfax, Virginia, USA.
Int J Numer Method Biomed Eng ; 39(8): e3740, 2023 08.
Article em En | MEDLINE | ID: mdl-37288602
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aneurisma Intracraniano Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aneurisma Intracraniano Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article