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

Gomez, A; Marcan, M; Arthurs, C; Wright, R; Youssefi, P; Jahangiri, M; Figueroa, A (2019) Optimal B-spline Mapping of Flow Imaging Data for Imposing Patient-specific Velocity Profiles in Computational Hemodynamics. IEEE Trans Biomed Eng, 66 (7). pp. 1872-1883. ISSN 1558-2531 https://doi.org/10.1109/TBME.2018.2880606
SGUL Authors: Jahangiri, Marjan

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Abstract

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.

Item Type: Article
Keywords: 0903 Biomedical Engineering, 0906 Electrical And Electronic Engineering, 0801 Artificial Intelligence And Image Processing, Biomedical Engineering
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: IEEE Trans Biomed Eng
ISSN: 1558-2531
Language: eng
Dates:
DateEvent
21 June 2019Published
11 December 2018Published Online
6 November 2018Accepted
Publisher License: Creative Commons: Attribution 3.0
Projects:
Project IDFunderFunder ID
U01 HL135842NHLBI NIH HHSUNSPECIFIED
102431Wellcome Trusthttp://dx.doi.org/10.13039/100004440
203148/Z/16/ZWellcome Trusthttp://dx.doi.org/10.13039/100004440
307532Seventh Framework Programmehttp://dx.doi.org/10.13039/501100004963
PubMed ID: 30561336
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/110488
Publisher's version: https://doi.org/10.1109/TBME.2018.2880606

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