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Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition

Overview of attention for article published in BioMedical Engineering OnLine, August 2016
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Title
Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition
Published in
BioMedical Engineering OnLine, August 2016
DOI 10.1186/s12938-016-0221-y
Pubmed ID
Authors

Hongbo Liu, Kun Wang, Jie Tian

Abstract

Positron emission tomography (PET) always suffers from high levels of noise due to the constraints of the injected dose and acquisition time, especially in the studies of dynamic PET imaging. To improve the quality of PET image, several approaches have been introduced to suppress noise. However, traditional filters often blur the image edges, or erase small detail, or rely on multiple parameters. In order to solve such problems, nonlocal denoising methods have been adapted to denoise PET images. In this paper, we propose to use the weighted higher-order singular value decomposition for PET image denoising. We first modeled the noise in the PET image as Poisson distribution. Then, we transformed the noise to an additive Gaussian noise by use of the anscombe root transformation. Finally, we denoised the transformed image using the proposed higher-order singular value decomposition (HOSVD)-based algorithms. The denoised results were compared with results from some general filters by performing physical phantom and mice studies. Compared to other commonly used filters, HOSVD-based denoising algorithms can preserve boundaries and quantitative accuracy better. The spatial resolution and the low activity features in PET image also can be preserved by use of HOSVD-based methods. Comparing with the standard HOSVD-based algorithm, the proposed weighted HOSVD algorithm can suppress the stair-step artifact, and the time-consumption is about half of that needed by the Wiener-augmented HOSVD algorithm. The proposed weighted HOSVD denoising algorithm can suppress noise while better preserving of boundary and quantity in PET images.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 34%
Student > Master 3 10%
Student > Postgraduate 3 10%
Researcher 3 10%
Student > Bachelor 2 7%
Other 3 10%
Unknown 5 17%
Readers by discipline Count As %
Engineering 9 31%
Physics and Astronomy 5 17%
Medicine and Dentistry 3 10%
Computer Science 2 7%
Psychology 1 3%
Other 2 7%
Unknown 7 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 August 2016.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from BioMedical Engineering OnLine
#607
of 867 outputs
Outputs of similar age
#272,818
of 349,676 outputs
Outputs of similar age from BioMedical Engineering OnLine
#20
of 21 outputs
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