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A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert…

Overview of attention for article published in Critical Reviews in Diagnostic Imaging, May 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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Title
A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data
Published in
Critical Reviews in Diagnostic Imaging, May 2016
DOI 10.1186/s12968-016-0242-5
Pubmed ID
Authors

Henrik Engblom, Jane Tufvesson, Robert Jablonowski, Marcus Carlsson, Anthony H. Aletras, Pavel Hoffmann, Alexis Jacquier, Frank Kober, Bernhard Metzler, David Erlinge, Dan Atar, Håkan Arheden, Einar Heiberg

Abstract

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CHILL-MI: NCT01379261 . NCT01374321 .

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X Demographics

The data shown below were collected from the profiles of 14 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 22%
Researcher 12 15%
Student > Master 6 7%
Student > Bachelor 5 6%
Student > Doctoral Student 5 6%
Other 12 15%
Unknown 23 28%
Readers by discipline Count As %
Medicine and Dentistry 25 31%
Engineering 10 12%
Computer Science 6 7%
Neuroscience 4 5%
Agricultural and Biological Sciences 3 4%
Other 5 6%
Unknown 28 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 14 June 2020.
All research outputs
#4,263,366
of 25,728,855 outputs
Outputs from Critical Reviews in Diagnostic Imaging
#253
of 1,386 outputs
Outputs of similar age
#61,952
of 313,275 outputs
Outputs of similar age from Critical Reviews in Diagnostic Imaging
#8
of 26 outputs
Altmetric has tracked 25,728,855 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 1,386 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 81% 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 313,275 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 80% of its contemporaries.
We're also able to compare this research output to 26 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 69% of its contemporaries.