Multi-modality 4D Flow MRI data enhancement for quantification of cerebral aneurysms hemodynamics.
This project is performed in collaboration with a team at Northwestern University and is funded by the NIH R21 NS106696 grant. The ultimate goal of this research is to develop a data fusion approach for augmenting MRI measurements of cerebral blood flow. Local hemodynamic forces can affect growth and rupture of cerebral aneurysms, potentially resulting in brain compression or stroke. The flow in cerebral aneurysms can be measured with time-resolved, three-directional MRI (4D flow MRI); however, the accuracy of the obtained velocity fields may be inadequate for reliable estimation of the relevant flow metrics. Alternatively, the flow can be modeled with computational fluid dynamics (CFD) ot particle image velocimetry (PIV). The modeling approach provides superior resolution but relies on various simplifications and assumptions. We work on integrating in vivo MR velocimetry with patient-specific computational and experimental models in order to attain high-fidelity and high-accuracy velocity fields, thus allowing us to reliably compute relevant hemodynamic metrics.
Computational modeling of postoperative flow in cerebral aneurysms.
In this project, funded by the NIH R01 HL115267 award, computational fluid dynamics (CFD) models of cerebral aneurysms are constructed from medical imaging data in order to predict blood flow fields that would result from alternative surgical and endovascular treatment options. Cerebral aneurysm patients considered for treatment at the University of California San Francisco, Medical College of Wisconsin and University of Arkansas for Medical Sciences are recruited for the study. Patient-specific CFD simulations of the flow and contrast transport in postoperative flow conditions predict intra-aneurysmal regions prone to thrombus deposition and thus help clinicians evaluate alternative interventional options.
Design and optimization of a chemofiltration device for removing chemotherapy toxins from blood.
Chemotherapy drugs injected intra-arterially to treat cancer can cause systemic toxic effects. A catheter-based Chemofilter device, temporarily deployed in a vein during the procedure can filter excessive drug from the blood thus reducing chemotherapy side-effects. This cross-disciplinary project, funded by the NIH R01 CA194533 grant, brings together clinicians, scientists and engineers from the University of California San Francisco, University of California – Berkeley, California Institute of Technology and Purdue, as well as industrial partners. The CFM lab’s objective is to optimize hemodynamic performance of the Chemofilter device. A multi-physics and multi-scale approach is used to simulate the transport and capture of the chemotherapy drugs. Developing computational models capable of coupling continuum mechanics with cellular interactions and intermolecular forces allows us to accurately predict filter efficiency and optimize its performance for specific agents and hemodynamic conditions. Alternative designs of the Chemofilter are evaluated in order to increase the contact area of the membrane, while minimizing its obstruction to the flow. The computational results are compared to in vitro experiments and animal studies conducted by our collaborators.
Image-based modeling of stress distribution in periventricular brain tissue.
In this project, we investigate the role of biomechanical factors in formation of white matter lesions in the brain. Computational models based on MRI data are used to estimate stress distribution in the white matter adjacent to brain ventricles due to pulsatile pressure and subject-specific anatomy of the ventricles.