. There is a growing interest in the neurosurgical community to assess hemodynamic risk factors that either remain or are eliminated after surgery that cannot be measured in vivo, but can be computed. Current CFD simulations merely address short segments, but are unable compute blood flow throughout the entire vascular tree. There is an unaddressed need to compute hemodynamic risk factors before and after endovascular interventions throughout the entire cerebral circulation. Whole-brain CFD flow simulation has not been accomplished before because of two unsolved problems. Problem 1. Current blood vessel segmentation methods would require weeks to reconstruct the entire vascular tree from angiography, which is clinically impractical. Problem 2. Even if the computational meshes could be constructed for the entire arterial tree, existing computers require excessive CPU time to solve the many embedded equations. These two problems are now solved by two innovations: Innovation 1. A new vessel segmentation pipeline largely automates the vessel reconstruction process and problem formulation. Segmentation with our current (not yet optimized) workflow takes less than 1 hour. Innovation 2. An image-based mesh generation technique generates a computer representation of the entire arterial tree from medical images with little or no need for technician intervention. The parametric mesh conforms to vessel centerlines to generate flow-dominated meshes. This enables the fast and reliable computation of hemodynamic metrics at a fraction of the mesh resolution needed in unstructured grids. Our preliminary work demonstrates that automatic tree segmentation and dynamic 3D CFD simulation of the entire arterial tree is attainable with regular desktop computer hardware. Because vascular modeling in TreeCFD is based on non-invasive magnetic resonance methods, testing of TreeCFD can be performed with both healthy volunteers and patients and will be achieved in two aims:
AIM 1. (With healthy volunteers) Test the performance of the automated platform for whole-tree cerebral hemodynamics with microcirculatory closure. Validate accuracy of vascular reconstruction and flow quantification.
AIM 2. (With stenosis patients.) Use TreeCFD to characterize the cerebral blood flow patterns before and after endovascular interventions and compare changes in all major hemodynamic indices of disturbed blood flow. Benefits. Availability of automated (real time) CFD simulations will provide surgeons with indicators for potential benefits and risks associated with endovascular procedures for individual patients. Tighter integration of imaging, endovascular interventions, and rigorous hemodynamic analysis will also eliminate barriers between surgeons and biomedical device designers aiming for better outcomes for cerebrovascular diseases.

Public Health Relevance

The proposed project is relevant to public health because high-speed and automatic entire cerebral tree simulation will visualize important hemodynamic indicators of potential risk for neurovascular surgeons. The technology of this proposal aligns with the mission of NINDS by providing clinicians with a priori information on the risks and benefits of treatment to improve treatment outcomes and reduce the burden of neurovascular disease.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Exploratory/Developmental Grants (R21)
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Bioengineering, Technology and Surgical Sciences Study Section (BTSS)
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Koenig, James I
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University of Illinois at Chicago
Biomedical Engineering
Schools of Medicine
United States
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Ghaffari, Mahsa; Sanchez, Lea; Xu, Guoren et al. (2018) Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics. Comput Biol Med 100:209-220
Ghaffari, Mahsa; Alaraj, Ali; Du, Xinjian et al. (2018) Quantification of near-wall hemodynamic risk factors in large-scale cerebral arterial trees. Int J Numer Method Biomed Eng 34:e2987
Ghaffari, Mahsa; Tangen, Kevin; Alaraj, Ali et al. (2017) Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation. Comput Biol Med 91:353-365