Optimal B-spline Mapping of Flow Imaging Data for Imposing Patient-specific Velocity Profiles in Computational Hemodynamics

Objective: We propose a novel method to map patient-specific blood velocity profiles (obtained from imaging data such as two-dimensional flow MRI or three-dimensional color Doppler ultrasound) to geometric vascular models suitable to perform computational fluid dynamics simulations of haemodynamics. We describe the implementation and utilization of the method within an open-source computational hemodynamics simulation software (CRIMSON). Methods: The proposed method establishes pointwise correspondences between the contour of a fixed geometric model and time-varying contours containing the velocity image data, from which a continuous, smooth, and cyclic deformation field is calculated. Our methodology is validated using synthetic data and demonstrated using two different in vivo aortic velocity datasets: a healthy subject with a normal tricuspid valve and a patient with a bicuspid aortic valve. Results: We compare our method with the state-of-the-art Schwarz–Christoffel method in terms of preservation of velocities and execution time. Our method is as accurate as the Schwarz–Christoffel method, while being over eight times faster. Conclusions: Our mapping method can accurately preserve either the flow rate or the velocity field through the surface and can cope with inconsistencies in motion and contour shape. Significance: The proposed method and its integration into the CRIMSON software enable a streamlined approach toward incorporating more patient-specific data in blood flow simulations.