Key result
Semi-automated CFD pipeline demonstrates high reliability versus manual workflows in intracranial aneurysm simulations.
Why the study?
Current computational fluid dynamics workflows for intracranial aneurysms are time-consuming, complex, and labor-intensive, limiting their fit in clinical environments.
Does a semi-automated open-source pipeline generate reliable computational fluid dynamics simulation results compared to manual workflows in patients with intracranial aneurysms?
Does a semi-automated open-source pipeline generate reliable computational fluid dynamics simulation results compared to manual workflows in patients with intracranial aneurysms?
A semi-automated, open-source pipeline for computational fluid dynamics simulations in intracranial aneurysms provides reliable geometric and hemodynamic results comparable to manual workflows.
Should not yet change aneurysm management; leaves open prospective validation of semi-automated CFD pipelines.
Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85-0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.
No takes yet. Share an insight, caveat, or question.
Rezaeitaleshmahalleh et al. (2024) studied Intracranial aneurysms (n=18). Semi-automated open-source pipeline for CFD simulations vs. Manual workflow was evaluated on Geometrical and computed hemodynamic variables. A semi-automated pipeline for computational fluid dynamics simulations in intracranial aneurysms demonstrated good to excellent reliability compared to manual workflows (ICC 0.85-0.95).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: