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Non-invasive kinetic modelling of PET tracers with radiometabolites using a constrained simultaneous estimation method: evaluation with 11C-SB201745

Overview of attention for article published in EJNMMI Research, July 2018
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About this Attention Score

  • Among the highest-scoring outputs from this source (#48 of 276)
  • Above-average Attention Score compared to outputs of the same age (60th percentile)

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21 Mendeley
Title
Non-invasive kinetic modelling of PET tracers with radiometabolites using a constrained simultaneous estimation method: evaluation with 11C-SB201745
Published in
EJNMMI Research, July 2018
DOI 10.1186/s13550-018-0412-6
Pubmed ID
Authors

Hasan Sari, Kjell Erlandsson, Lisbeth Marner, Ian Law, Henrik B.W. Larsson, Kris Thielemans, Sébastien Ourselin, Simon Arridge, David Atkinson, Brian F. Hutton

Abstract

Kinetic analysis of dynamic PET data requires an accurate knowledge of available PET tracer concentration within blood plasma over time, known as the arterial input function (AIF). The gold standard method used to measure the AIF requires serial arterial blood sampling over the course of the PET scan, which is an invasive procedure and makes this method less practical in clinical settings. Traditional image-derived methods are limited to specific tracers and are not accurate if metabolites are present in the plasma. In this work, we utilise an image-derived whole blood curve measurement to reduce the computational complexity of the simultaneous estimation method (SIME), which is capable of estimating the AIF directly from tissue time activity curves (TACs). This method was applied to data obtained from a serotonin receptor study (11C-SB207145) and estimated parameter results are compared to results obtained using the original SIME and gold standard AIFs derived from arterial samples. Reproducibility of the method was assessed using test-retest data. It was shown that the incorporation of image-derived information increased the accuracy of total volume of distribution (V T) estimates, averaged across all regions, by 40% and non-displaceable binding potential (BP ND) estimates by 16% compared to the original SIME. Particular improvements were observed in K1 parameter estimates. BP ND estimates, based on the proposed method and the gold standard arterial sample-derived AIF, were not significantly different (P=0.7). The results of this work indicate that the proposed method with prior AIF information obtained from a partial volume corrected image-derived whole blood curve, and modelled parent fraction, has the potential to be used as an alternative non-invasive method to perform kinetic analysis of tracers with metabolite products.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 43%
Researcher 7 33%
Lecturer 1 5%
Student > Master 1 5%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 1 5%
Readers by discipline Count As %
Neuroscience 6 29%
Physics and Astronomy 4 19%
Medicine and Dentistry 4 19%
Engineering 2 10%
Psychology 1 5%
Other 3 14%
Unknown 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 July 2018.
All research outputs
#4,151,052
of 13,801,769 outputs
Outputs from EJNMMI Research
#48
of 276 outputs
Outputs of similar age
#103,410
of 271,077 outputs
Outputs of similar age from EJNMMI Research
#1
of 1 outputs
Altmetric has tracked 13,801,769 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 276 research outputs from this source. They receive a mean Attention Score of 1.8. 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 271,077 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 60% of its contemporaries.
We're also able to compare this research output to 1 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