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Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification

Overview of attention for article published in Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), October 2015
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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 (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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12 tweeters

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46 Mendeley
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Title
Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification
Published in
Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), October 2015
DOI 10.1186/s12968-015-0190-5
Pubmed ID
Authors

Mariana Bustamante, Sven Petersson, Jonatan Eriksson, Urban Alehagen, Petter Dyverfeldt, Carl-Johan Carlhäll, Tino Ebbers

Abstract

Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r (2)=0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r (2)=0.955), pulmonary trunk vs. pulmonary branches (r (2)=0.808), and pulmonary trunk vs. caval veins (r (2)=0.906). The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
Spain 1 2%
Australia 1 2%
United States 1 2%
Sweden 1 2%
Canada 1 2%
Unknown 39 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 26%
Researcher 10 22%
Student > Master 7 15%
Student > Doctoral Student 6 13%
Student > Bachelor 4 9%
Other 7 15%
Readers by discipline Count As %
Medicine and Dentistry 21 46%
Engineering 13 28%
Unspecified 4 9%
Computer Science 4 9%
Agricultural and Biological Sciences 2 4%
Other 2 4%

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 25 October 2015.
All research outputs
#1,792,977
of 11,230,767 outputs
Outputs from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#155
of 746 outputs
Outputs of similar age
#50,735
of 247,941 outputs
Outputs of similar age from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#6
of 25 outputs
Altmetric has tracked 11,230,767 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 746 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% 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 247,941 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 79% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.