↓ Skip to main content

Fractal frontiers in cardiovascular magnetic resonance: towards clinical implementation

Overview of attention for article published in Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), September 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)

Mentioned by

twitter
11 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
50 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Fractal frontiers in cardiovascular magnetic resonance: towards clinical implementation
Published in
Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), September 2015
DOI 10.1186/s12968-015-0179-0
Pubmed ID
Authors

Gabriella Captur, Audrey L. Karperien, Chunming Li, Filip Zemrak, Catalina Tobon-Gomez, Xuexin Gao, David A. Bluemke, Perry M. Elliott, Steffen E. Petersen, James C. Moon

Abstract

Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain.This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community.By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Student > Ph. D. Student 10 20%
Student > Master 8 16%
Professor > Associate Professor 7 14%
Other 4 8%
Other 5 10%
Unknown 5 10%
Readers by discipline Count As %
Medicine and Dentistry 17 34%
Engineering 6 12%
Neuroscience 4 8%
Computer Science 3 6%
Agricultural and Biological Sciences 3 6%
Other 6 12%
Unknown 11 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 29 June 2020.
All research outputs
#3,082,648
of 15,928,103 outputs
Outputs from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#255
of 999 outputs
Outputs of similar age
#51,911
of 242,854 outputs
Outputs of similar age from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#1
of 1 outputs
Altmetric has tracked 15,928,103 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 999 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 73% 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 242,854 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them