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An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Overview of attention for article published in BMC Medical Imaging, January 2016
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  • High Attention Score compared to outputs of the same age and source (90th percentile)

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57 Mendeley
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Title
An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited
Published in
BMC Medical Imaging, January 2016
DOI 10.1186/s12880-016-0109-0
Pubmed ID
Authors

Frank G. Zöllner, Markus Daab, Steven P. Sourbron, Lothar R. Schad, Stefan O. Schoenberg, Gerald Weisser

Abstract

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology. Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft's model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters. We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
United States 1 2%
Unknown 54 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 30%
Student > Master 8 14%
Student > Ph. D. Student 7 12%
Professor > Associate Professor 5 9%
Other 4 7%
Other 4 7%
Unknown 12 21%
Readers by discipline Count As %
Medicine and Dentistry 24 42%
Engineering 7 12%
Computer Science 3 5%
Physics and Astronomy 3 5%
Social Sciences 2 4%
Other 5 9%
Unknown 13 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 27 December 2020.
All research outputs
#6,156,186
of 22,840,638 outputs
Outputs from BMC Medical Imaging
#72
of 596 outputs
Outputs of similar age
#99,463
of 395,720 outputs
Outputs of similar age from BMC Medical Imaging
#1
of 10 outputs
Altmetric has tracked 22,840,638 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 596 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done well, scoring higher than 87% 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 395,720 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 74% of its contemporaries.
We're also able to compare this research output to 10 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