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Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge

Overview of attention for article published in Cardiovascular Engineering and Technology, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 180)
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Title
Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge
Published in
Cardiovascular Engineering and Technology, September 2018
DOI 10.1007/s13239-018-00374-2
Pubmed ID
Authors

Kristian Valen-Sendstad, Aslak W. Bergersen, Yuji Shimogonya, Leonid Goubergrits, Jan Bruening, Jordi Pallares, Salvatore Cito, Senol Piskin, Kerem Pekkan, Arjan J. Geers, Ignacio Larrabide, Saikiran Rapaka, Viorel Mihalef, Wenyu Fu, Aike Qiao, Kartik Jain, Sabine Roller, Kent-Andre Mardal, Ramji Kamakoti, Thomas Spirka, Neil Ashton, Alistair Revell, Nicolas Aristokleous, J. Graeme Houston, Masanori Tsuji, Fujimaro Ishida, Prahlad G. Menon, Leonard D. Browne, Stephen Broderick, Masaaki Shojima, Satoshi Koizumi, Michael Barbour, Alberto Aliseda, Hernán G. Morales, Thierry Lefèvre, Simona Hodis, Yahia M. Al-Smadi, Justin S. Tran, Alison L. Marsden, Sreeja Vaippummadhom, G. Albert Einstein, Alistair G. Brown, Kristian Debus, Kuniyasu Niizuma, Sherif Rashad, Shin-ichiro Sugiyama, M. Owais Khan, Adam R. Updegrove, Shawn C. Shadden, Bart M. W. Cornelissen, Charles B. L. M. Majoie, Philipp Berg, Sylvia Saalfield, Kenichi Kono, David A. Steinman

Abstract

Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 113 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 20%
Researcher 19 17%
Student > Master 15 13%
Student > Bachelor 9 8%
Student > Doctoral Student 5 4%
Other 17 15%
Unknown 25 22%
Readers by discipline Count As %
Engineering 47 42%
Medicine and Dentistry 16 14%
Computer Science 5 4%
Agricultural and Biological Sciences 3 3%
Energy 3 3%
Other 8 7%
Unknown 31 27%
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 28 September 2022.
All research outputs
#5,618,284
of 23,508,125 outputs
Outputs from Cardiovascular Engineering and Technology
#17
of 180 outputs
Outputs of similar age
#95,943
of 338,303 outputs
Outputs of similar age from Cardiovascular Engineering and Technology
#2
of 8 outputs
Altmetric has tracked 23,508,125 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 180 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 91% 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 338,303 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 71% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.