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A247 Use of a physics informed neural network (pinns) for in-vivo quantification of csf flow
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A247 Use of a physics informed neural network (pinns) for in-vivo quantification of csf flow
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Journal Article
A247 Use of a physics informed neural network (pinns) for in-vivo quantification of csf flow
2025
Overview
IntroductionComputational fluid dynamics (CFD) has long been used to model cerebrospinal fluid (CSF) flow in virtual environments, allowing exploration of complex interactions that are difficult to study in vivo. However, CFD faces significant challenges, including its reliance on non-intersecting meshes and the requirement to define boundary conditions—both of which are difficult to obtain in biological systems. Physics-Informed Neural Networks (PINNs), on the other hand, have emerged as a powerful alternative for modeling three-dimensional (3D) flow fields from sparse or incomplete data.Aim of StudyTo reconstruct 3D CSF flow fields using a PINN model trained on planar projection data extracted from CFD simulations in anatomically realistic geometries.MethodA three-dimensional (3D) reconstruction of a canine SAS segment was used, produced from in-vivo real time imaging of the SAS using intravascular high-frequency optical coherence tomography (HF-OCT). CFD simulations of CSF flow within this segment were performed, and planar projection velocity data were extracted. A dedicated PINN model, developed in-house, was trained on this projection data to reconstruct the full 3D velocity field within the sameResultsThe PINN model, initially trained on simple anatomical geometries, yielded results comparable to full CFD simulations. When applied to the complex SAS geometry, the PINN reconstructions achieved less than 10% error relative to CFD predictions.Abstract A247 Figure 1Illustration of multiple arbitraily oriented acquisition planes within a canine subarachnoid space SAS (left) and 3D-reconstruction of simulated CSF flow using a physics informed neural network (PINN) (right). The anatomical data were produced from in vivo real time imaging of the SAS with intravascular HF-OCT[Image Omitted. See PDF.]ConclusionThese findings demonstrate that PINNs can accurately reconstruct complex CSF flow patterns from limited data, offering a promising alternative to traditional CFD by reducing the need for detailed boundary conditions and mesh construction in biological systems.Conflict of InterestNo
Publisher
BMJ Publishing Group LTD
Subject
