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SUPRA: open-source software-defined ultrasound processing for real-time applications

Overview of attention for article published in International Journal of Computer Assisted Radiology and Surgery, March 2018
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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 (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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13 X users

Citations

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24 Dimensions

Readers on

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25 Mendeley
Title
SUPRA: open-source software-defined ultrasound processing for real-time applications
Published in
International Journal of Computer Assisted Radiology and Surgery, March 2018
DOI 10.1007/s11548-018-1750-6
Pubmed ID
Authors

Rüdiger Göbl, Nassir Navab, Christoph Hennersperger

Abstract

Research in ultrasound imaging is limited in reproducibility by two factors: First, many existing ultrasound pipelines are protected by intellectual property, rendering exchange of code difficult. Second, most pipelines are implemented in special hardware, resulting in limited flexibility of implemented processing steps on such platforms. With SUPRA, we propose an open-source pipeline for fully software-defined ultrasound processing for real-time applications to alleviate these problems. Covering all steps from beamforming to output of B-mode images, SUPRA can help improve the reproducibility of results and make modifications to the image acquisition mode accessible to the research community. We evaluate the pipeline qualitatively, quantitatively, and regarding its run time. The pipeline shows image quality comparable to a clinical system and backed by point spread function measurements a comparable resolution. Including all processing stages of a usual ultrasound pipeline, the run-time analysis shows that it can be executed in 2D and 3D on consumer GPUs in real time. Our software ultrasound pipeline opens up the research in image acquisition. Given access to ultrasound data from early stages (raw channel data, radiofrequency data), it simplifies the development in imaging. Furthermore, it tackles the reproducibility of research results, as code can be shared easily and even be executed without dedicated ultrasound hardware.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 28%
Researcher 4 16%
Student > Ph. D. Student 3 12%
Other 2 8%
Student > Bachelor 2 8%
Other 3 12%
Unknown 4 16%
Readers by discipline Count As %
Engineering 9 36%
Computer Science 5 20%
Medicine and Dentistry 5 20%
Materials Science 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 0 0%
Unknown 4 16%
Attention Score in Context

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 June 2018.
All research outputs
#5,431,262
of 25,382,440 outputs
Outputs from International Journal of Computer Assisted Radiology and Surgery
#104
of 964 outputs
Outputs of similar age
#97,866
of 344,304 outputs
Outputs of similar age from International Journal of Computer Assisted Radiology and Surgery
#8
of 28 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 964 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 89% 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 344,304 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 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.