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Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI

Overview of attention for article published in Scientific Reports, April 2017
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Title
Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
Published in
Scientific Reports, April 2017
DOI 10.1038/srep46618
Pubmed ID
Authors

Hojin Ha, Jonas Lantz, Magnus Ziegler, Belen Casas, Matts Karlsson, Petter Dyverfeldt, Tino Ebbers

Abstract

The pressure drop across a stenotic vessel is an important parameter in medicine, providing a commonly used and intuitive metric for evaluating the severity of the stenosis. However, non-invasive estimation of the pressure drop under pathological conditions has remained difficult. This study demonstrates a novel method to quantify the irreversible pressure drop across a stenosis using 4D Flow MRI by calculating the total turbulence production of the flow. Simulation MRI acquisitions showed that the energy lost to turbulence production can be accurately quantified with 4D Flow MRI within a range of practical spatial resolutions (1-3 mm; regression slope = 0.91, R(2) = 0.96). The quantification of the turbulence production was not substantially influenced by the signal-to-noise ratio (SNR), resulting in less than 2% mean bias at SNR > 10. Pressure drop estimation based on turbulence production robustly predicted the irreversible pressure drop, regardless of the stenosis severity and post-stenosis dilatation (regression slope = 0.956, R(2) = 0.96). In vitro validation of the technique in a 75% stenosis channel confirmed that pressure drop prediction based on the turbulence production agreed with the measured pressure drop (regression slope = 1.15, R(2) = 0.999, Bland-Altman agreement = 0.75 ± 3.93 mmHg).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 111 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 20%
Researcher 14 13%
Student > Doctoral Student 11 10%
Student > Bachelor 8 7%
Student > Master 7 6%
Other 15 14%
Unknown 34 31%
Readers by discipline Count As %
Engineering 41 37%
Medicine and Dentistry 13 12%
Psychology 2 2%
Neuroscience 2 2%
Mathematics 2 2%
Other 5 5%
Unknown 46 41%
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 20 April 2017.
All research outputs
#20,414,746
of 22,965,074 outputs
Outputs from Scientific Reports
#105,986
of 123,986 outputs
Outputs of similar age
#269,868
of 310,204 outputs
Outputs of similar age from Scientific Reports
#3,456
of 4,226 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 123,986 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4,226 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.