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Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Overview of attention for article published in EJNMMI Research, April 2017
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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 (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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1 news outlet

Citations

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22 Dimensions

Readers on

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39 Mendeley
Title
Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising
Published in
EJNMMI Research, April 2017
DOI 10.1186/s13550-017-0284-1
Pubmed ID
Authors

Sandeep S. V. Golla, Mark Lubberink, Bart N. M. van Berckel, Adriaan A. Lammertsma, Ronald Boellaard

Abstract

Accurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM. An anthropomorphic Hoffman brain phantom was filled with an [(18)F]FDG solution of ~25 kBq mL(-1) and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [(11)C]flumazenil (five healthy subjects) and [(11)C]PIB (four healthy subjects and four patients with Alzheimer's disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V T) across various regions of the brain. In the case of [(11)C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V T whereas a 0 to 10% increase or decrease was seen in the case of [(11)C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V Ts from the PVE-uncorrected scans. IDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Researcher 8 21%
Student > Master 5 13%
Student > Bachelor 2 5%
Librarian 2 5%
Other 6 15%
Unknown 8 21%
Readers by discipline Count As %
Medicine and Dentistry 8 21%
Engineering 6 15%
Physics and Astronomy 5 13%
Computer Science 3 8%
Neuroscience 3 8%
Other 6 15%
Unknown 8 21%
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 28 April 2017.
All research outputs
#4,211,224
of 22,968,808 outputs
Outputs from EJNMMI Research
#54
of 563 outputs
Outputs of similar age
#74,598
of 309,918 outputs
Outputs of similar age from EJNMMI Research
#4
of 15 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 563 research outputs from this source. They receive a mean Attention Score of 2.5. This one has done well, scoring higher than 86% 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 309,918 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 15 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 66% of its contemporaries.