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Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI

Overview of attention for article published in BioMedical Engineering OnLine, November 2015
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Title
Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI
Published in
BioMedical Engineering OnLine, November 2015
DOI 10.1186/s12938-015-0104-7
Pubmed ID
Authors

Vinicius C. Rispoli, Jon F. Nielsen, Krishna S. Nayak, Joao L. A. Carvalho

Abstract

Phase contrast magnetic resonance imaging (PC-MRI) is used clinically for quantitative assessment of cardiovascular flow and function, as it is capable of providing directly-measured 3D velocity maps. Alternatively, vascular flow can be estimated from model-based computation fluid dynamics (CFD) calculations. CFD provides arbitrarily high resolution, but its accuracy hinges on model assumptions, while velocity fields measured with PC-MRI generally do not satisfy the equations of fluid dynamics, provide limited resolution, and suffer from partial volume effects. The purpose of this study is to develop a proof-of-concept numerical procedure for constructing a simulated flow field that is influenced by both direct PC-MRI measurements and a fluid physics model, thereby taking advantage of both the accuracy of PC-MRI and the high spatial resolution of CFD. The use of the proposed approach in regularizing 3D flow fields is evaluated. The proposed algorithm incorporates both a Newtonian fluid physics model and a linear PC-MRI signal model. The model equations are solved numerically using a modified CFD algorithm. The numerical solution corresponds to the optimal solution of a generalized Tikhonov regularization, which provides a flow field that satisfies the flow physics equations, while being close enough to the measured PC-MRI velocity profile. The feasibility of the proposed approach is demonstrated on data from the carotid bifurcation of one healthy volunteer, and also from a pulsatile carotid flow phantom. The proposed solver produces flow fields that are in better agreement with direct PC-MRI measurements than CFD alone, and converges faster, while closely satisfying the fluid dynamics equations. For the implementation that provided the best results, the signal-to-error ratio (with respect to the PC-MRI measurements) in the phantom experiment was 6.56 dB higher than that of conventional CFD; in the in vivo experiment, it was 2.15 dB higher. The proposed approach allows partial or complete measurements to be incorporated into a modified CFD solver, for improving the accuracy of the resulting flow fields estimates. This can be used for reducing scan time, increasing the spatial resolution, and/or denoising the PC-MRI measurements.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
South Africa 1 1%
Unknown 95 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 20%
Researcher 13 13%
Student > Master 13 13%
Student > Bachelor 11 11%
Student > Postgraduate 6 6%
Other 16 16%
Unknown 19 19%
Readers by discipline Count As %
Engineering 50 51%
Medicine and Dentistry 4 4%
Mathematics 4 4%
Chemical Engineering 3 3%
Biochemistry, Genetics and Molecular Biology 3 3%
Other 9 9%
Unknown 25 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 November 2015.
All research outputs
#18,825,900
of 23,330,477 outputs
Outputs from BioMedical Engineering OnLine
#574
of 834 outputs
Outputs of similar age
#282,252
of 389,816 outputs
Outputs of similar age from BioMedical Engineering OnLine
#28
of 38 outputs
Altmetric has tracked 23,330,477 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 834 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.