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A new CFD based non-invasive method for functional diagnosis of coronary stenosis

Overview of attention for article published in BioMedical Engineering OnLine, March 2018
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Title
A new CFD based non-invasive method for functional diagnosis of coronary stenosis
Published in
BioMedical Engineering OnLine, March 2018
DOI 10.1186/s12938-018-0468-6
Pubmed ID
Authors

Xinzhou Xie, Minwen Zheng, Didi Wen, Yabing Li, Songyun Xie

Abstract

Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFRCT) for functional measurement of stenosis. However, the accuracy of FFRCT is limited due to the approximate modeling approach of maximal hyperemia conditions. To overcome this problem, a new CFD based non-invasive method is proposed. Instead of modeling maximal hyperemia condition, a series of boundary conditions are specified and those simulated results are combined to provide a pressure-flow curve for a stenosis. Then, functional diagnosis of stenosis is assessed based on parameters derived from the obtained pressure-flow curve. The proposed method is applied to both idealized and patient-specific models, and validated with invasive FFR in six patients. Results show that additional hemodynamic information about the flow resistances of a stenosis is provided, which cannot be directly obtained from anatomy information. Parameters derived from the simulated pressure-flow curve show a linear and significant correlations with invasive FFR (r > 0.95, P < 0.05). The proposed method can assess flow resistances by the pressure-flow curve derived parameters without modeling of maximal hyperemia condition, which is a new promising approach for non-invasive functional assessment of coronary stenosis.

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Mendeley readers

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The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 15%
Researcher 6 13%
Student > Ph. D. Student 6 13%
Student > Bachelor 3 6%
Student > Postgraduate 3 6%
Other 6 13%
Unknown 16 34%
Readers by discipline Count As %
Engineering 16 34%
Medicine and Dentistry 6 13%
Unspecified 2 4%
Physics and Astronomy 1 2%
Mathematics 1 2%
Other 2 4%
Unknown 19 40%
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 09 May 2018.
All research outputs
#18,606,163
of 23,047,237 outputs
Outputs from BioMedical Engineering OnLine
#564
of 824 outputs
Outputs of similar age
#258,253
of 332,510 outputs
Outputs of similar age from BioMedical Engineering OnLine
#10
of 15 outputs
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So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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