Using Computer Modeling to Predict Cerebral Aneurysms
Cerebral aneurysms are among the fatal cerebrovascular diseases, exhibiting high rates of morbidity and mortality worldwide. Several risk factors contribute to the development of cerebral aneurysms. Wall Shear Stress (WSS) has proven to be a key hemodynamic factor inducing cerebral aneurysms. Numerical studies have utilized non-biological models to investigate WSS and dynamic pressure, employing computational fluid dynamics (CFD) simulations. Pulsatile flow over an open cavity has recently served as a classic benchmark, representing physiological phenomena in conducting in-vitro experiments for cerebral sidewall aneurysms. However, not all fluid dynamics characteristics of interest can be easily measured through emulational experiments in vitro. Thus, from a fluid dynamics perspective across aneurysms, several research questions remain unanswered: Where are the most informative dynamics located? And how can we use more readily available measurements to predict less available variables, better indicating potential locations and the formation of cerebral aneurysms?
Assistant Professor Ruihang Zhang (U of M Duluth, Mechanical and Industrial Engineering) and Assistant Professor Kun Zhang (U of M Duluth, Civil Engineering) are working on a project called “Prediction of Wall Shear Stress in Open Cavity Models: An Integrated Study using CFD models and Data-Driven Sparse Sensing,” that seeks to integrate a data-driven sparse sensing technique with CFD models to identify the optimal locations to represent the fluid dynamics in the open-cavity model and reconstruct two-dimensional WSS profiles based on minimal measurements. Identifying the representative locations in open-cavity models and predicting WSS profiles can help predict/classify if a patient is potentially prone to the development of brain aneurysms.
This project recently received a DSI Small Seed Grant. DSI Seed Grant funds are intended to promote, catalyze, accelerate, and advance U of M-based data science research so that U of M faculty and staff are well prepared to compete for longer term external funding opportunities. Priority is given to projects that will enable or bridge applications to larger funding opportunities and/or create new cross-disciplinary or cross-system collaborations, and to those that align with at least one of the areas of the MnDRIVE initiative. Projects must align with one of the current DSI focus areas, Foundational Data Sciences or Digital Health and Personalized Health Care Delivery. This project falls under the Brain Conditions research area of the MnDRIVE initiative and the Foundational Data Sciences focus area.
