King's College London

Research portal

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

Research output: Contribution to journalArticlepeer-review

Alberto Gomez, Marija Marcan, Christopher Arthurs, Robert Wright, Pouya Youssefi, Marjan Jahangiri, Alberto Figueroa

Original languageEnglish
Pages (from-to)1872-1883
JournalIEEE Transactions on Biomedical Engineering
Issue number7
Early online date11 Dec 2018
Accepted/In press6 Nov 2018
E-pub ahead of print11 Dec 2018
PublishedJul 2019


King's Authors


Objective: We propose a novel method to map patient-specific blood velocity profiles obtained from imaging data such as 2D flow MRI or 3D colour Doppler ultrasound) to geometric vascular models suitable to perform CFD simulations of haemodynamics. We describe the implementation and utilisation of the method within an open-source computational hemodynamics simulation software (CRIMSON). Methods: The proposed method establishes point-wise 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 normal tricuspid valve and a patient with 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 8 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 towards incorporating more patient-specific data in blood flow simulations.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454