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Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification

Overview of attention for article published in Critical Reviews in Diagnostic Imaging, April 2017
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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26 X users
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1 Facebook page

Citations

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213 Dimensions

Readers on

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191 Mendeley
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1 CiteULike
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Title
Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
Published in
Critical Reviews in Diagnostic Imaging, April 2017
DOI 10.1186/s12968-017-0355-5
Pubmed ID
Authors

Peter Kellman, Michael S. Hansen, Sonia Nielles-Vallespin, Jannike Nickander, Raquel Themudo, Martin Ugander, Hui Xue

Abstract

Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium. A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress. For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively. A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 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 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 191 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 21%
Student > Ph. D. Student 28 15%
Other 15 8%
Student > Doctoral Student 15 8%
Student > Master 10 5%
Other 31 16%
Unknown 52 27%
Readers by discipline Count As %
Medicine and Dentistry 72 38%
Engineering 26 14%
Computer Science 7 4%
Physics and Astronomy 5 3%
Psychology 3 2%
Other 14 7%
Unknown 64 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 04 June 2018.
All research outputs
#2,271,895
of 25,593,129 outputs
Outputs from Critical Reviews in Diagnostic Imaging
#91
of 1,382 outputs
Outputs of similar age
#41,728
of 325,215 outputs
Outputs of similar age from Critical Reviews in Diagnostic Imaging
#3
of 21 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,382 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 particularly well, scoring higher than 93% 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 325,215 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 87% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.