Cerebral aneurysms are dilatations of arteries feeding the brain that can present a danger of rupture, brain compression, and thrombotic occlusion of downstream vessels. A number of studies demonstrated that progression of cerebral aneurysms is affected by hemodynamic forces. Intra-aneurysmal flow dynamics can be quantified with in vivo MRI velocimetry (4D Flow MRI) or with patient-specific models based on imaging data. The 4D Flow MRI is capable of measuring three-dimensional velocity fields through the cardiac cycle, however its limited spatiotemporal resolution and dynamic velocity range may affect the accuracy of the resulting flow metric. Computational Fluid Dynamics (CFD) can provide superior resolution, but the reliability of the simulations depends on modeling assumptions and uncertainty of the vascular geometries and boundary conditions obtained from the imaging data. Particle Image Velocimetry (PIV) can be used to acquire experimental measurements in 3D-printed flow phantoms replicating cerebral aneurysm geometries. The PIV resolution in space and time is comparable to that of CFD models, but the accuracy can be affected by experimental setup, e.g. inlet and outlet flow and pressure waveforms, properties of the working fluid and transparency of the phantom’s material. In our studies, these alternative flow quantification modalities are used to complement each other in order to reduce errors and improve the accuracy of the flow analysis.
An important advantage of the CFD models is the ability to predict postoperative flow fields that would result from different treatment options. In some cases, when an aneurysm cannot be completely removed from the circulation, it can be treated by altering the flow patterns with either a surgery or a flow-diverting stent. Despite their advantages, such treatments introduce complications related to undesired occlusion of vital branching arteries with thrombus. In this talk, some of the cases where image-based CFD analysis was used to provide valuable information to clinicians will be described. Computational models constructed from MR images of the preoperative vascular geometries and flow conditions were modified in order to simulate flow changes caused by alternative surgeries. Numerical solutions for the flow fields were obtained and transport of a virtual contrast agent was modeled in order to estimate flow residence time and determine postoperative regions prone to thrombus deposition. These results demonstrate the potential of image-based CFD models to provide guidance in diagnostics and treatment of cerebral aneurysms.
Bio: Dr. Rayz's research is focused on blood flow modeling and analysis in order to improve diagnostics and treatment of cardiovascular disease. Dr. Rayz earned his PhD in Mechanical Engineering from the University of California, Berkeley in 2005. He joined the Vascular Imaging Research Center at the University of California San Francisco (UCSF) as a postdoctoral fellow and then, after receiving a K25 NIH award, worked as a research scientist at the UCSF Radiology department. In 2014 Dr. Rayz received a joint appointment as the faculty in the College of Engineering at the University of Wisconsin - Milwaukee (UWM) and in Neurosurgery at the Medical College of Wisconsin (MCW). In 2017 Dr. Rayz joined Weldon School of Biomedical Engineering at Purdue University. He also holds a courtesy appointment in Mechanical Engineering at Purdue. Dr. Rayz collaborates with clinicians and scientists at the UC San Francisco, Northwestern University, University of Lyon. Dr. Rayz’s research on computational modeling of post-surgical flow in brain aneurysms is funded by the NIH. He is also involved in modeling biomedical devices, including NIH-funded projects on flow diverter stents and on chemofiltration devices removing chemotherapy drugs from blood. Dr. Rayz’s research results are published in many journals publications as well as presented in numerous national and international conferences.