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Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping

Overview of attention for article published in Critical Reviews in Diagnostic Imaging, March 2021
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
5 X users
patent
1 patent

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
68 Mendeley
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Title
Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping
Published in
Critical Reviews in Diagnostic Imaging, March 2021
DOI 10.1186/s12968-021-00712-9
Pubmed ID
Authors

Sona Ghadimi, Daniel A. Auger, Xue Feng, Changyu Sun, Craig H. Meyer, Kenneth C. Bilchick, Jie Jane Cao, Andrew D. Scott, John N. Oshinski, Daniel B. Ennis, Frederick H. Epstein

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 16%
Student > Ph. D. Student 9 13%
Student > Master 5 7%
Student > Doctoral Student 5 7%
Other 4 6%
Other 8 12%
Unknown 26 38%
Readers by discipline Count As %
Engineering 16 24%
Medicine and Dentistry 14 21%
Computer Science 8 12%
Physics and Astronomy 1 1%
Neuroscience 1 1%
Other 1 1%
Unknown 27 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 25 August 2022.
All research outputs
#6,612,436
of 25,728,855 outputs
Outputs from Critical Reviews in Diagnostic Imaging
#461
of 1,386 outputs
Outputs of similar age
#140,633
of 454,352 outputs
Outputs of similar age from Critical Reviews in Diagnostic Imaging
#9
of 40 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,386 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 454,352 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.