Develop a High-fidelity Paradigm to Investigate Hemodynamic Risks in Cerebral Aneurysms

Quantifying hemodynamic risks in cerebral aneurysms (CAs) is pivotal for clinical surveillance and intervention. However, a high-fidelity paradigm to secure hemodynamic parameters using computational fluid dynamics (CFD) modeling is still under-developed upon; thus, most existing hemodynamic studies are not applicable in neuro-interventional practice. To bridge such a knowledge gap, we developed several high-fidelity pathways to secure accurate computational hemodynamic information in CAs. More specifically, (a) the standard procedures (i.e., qualified DSA images, noise reduce, appropriate smoothing algorithms) were created for the anatomical model reconstruction of CAs, which can help research peers to reproduce the anatomical arterial and aneurysmal models. (b) A pathway of meshing arterial models, especially near-wall regions, and investigating mesh independence was designed to secure the proper mesh quality for CFD modeling by considering the balance of modeling cost and accuracy. (c) An in-vitro method was developed to validate the non-Newtonian CFD CA model using reliable experimental data (i.e., particle image velocimetry (PIV) measurements) on the same CA model involving: (1) the blood non-Newtonian viscosity-shear rate model which was derived from experimental tests; and (2) laminar-to-turbulence pulsatile blood flow model. The patient-specific CFD CA model was validated via the acceptable congruency of hemodynamic parameters between in-silico and in-vitro results using PIV measurements and CFD simulation at three designated time stations within a pulsatile cardiac cycle. (d) A pathway to obtain accurate patient-specific BCs for hemodynamic computation was developed using transcranial Doppler (TCD) ultrasonography measurements and the discrete Fourier transform (DFT) simulation. Using the described high-fidelity paradigm pathways to conduct hemodynamic predictions can strengthen the CA research results and provide more accurate hemodynamic risk information for the diagnosis of CA pathophysiology.