Denoising of PET images by context modelling using local neighbourhood correlation

Positron emission tomography (PET) images are characterised by low signal-to-noise ratio and blurred edges when compared with other image modalities. It is therefore advisable to use noise reduction methods for qualitative and quantitative analyses. Given the importance of the maximum and mean uptak...

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Bibliographic Details
Published in:Physics in medicine & biology 2017-01, Vol.62 (2), p.633-651
Main Authors: Huerga, Carlos, Castro, Pablo, Corredoira, Eva, Coronado, Monica, Delgado, Victor, Guibelalde, Eduardo
Format: Article
Language:English
Subjects:
Algorithms
autocorrelation function
denoising
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Phantoms, Imaging
positron emission tomography
Positron-Emission Tomography - methods
Signal-To-Noise Ratio
wavelet transform
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Summary:Positron emission tomography (PET) images are characterised by low signal-to-noise ratio and blurred edges when compared with other image modalities. It is therefore advisable to use noise reduction methods for qualitative and quantitative analyses. Given the importance of the maximum and mean uptake values, it is necessary to avoid signal loss, which could modify the clinical significance. This paper proposes a method of non-linear image denoising for PET. It is based on spatially adaptive wavelet-shrinkage and uses context modelling, which explicitly considers the correlation between neighbouring pixels. This context modelling is able to maintain the uptake values and preserve the edges in significant regions. The algorithm is proposed as an alternative to the usual filtering that is performed after reconstruction.
ISSN:0031-9155
1361-6560
DOI:10.1088/1361-6560/62/2/633