Non-local means denoising of dynamic PET images.

<h4>Objective</h4>Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fit...

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Autores principales:	Joyita Dutta, Richard M Leahy, Quanzheng Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3a72f2b090c403ba2963be15238c49a
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spelling	oai:doaj.org-article:e3a72f2b090c403ba2963be15238c49a2021-11-18T08:43:21ZNon-local means denoising of dynamic PET images.1932-620310.1371/journal.pone.0081390https://doaj.org/article/e3a72f2b090c403ba2963be15238c49a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24339921/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objective</h4>Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).<h4>Theory</h4>NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.<h4>Methods</h4>To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.<h4>Results</h4>The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.Joyita DuttaRichard M LeahyQuanzheng LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e81390 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joyita Dutta Richard M Leahy Quanzheng Li Non-local means denoising of dynamic PET images.
description	<h4>Objective</h4>Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).<h4>Theory</h4>NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.<h4>Methods</h4>To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.<h4>Results</h4>The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
format	article
author	Joyita Dutta Richard M Leahy Quanzheng Li
author_facet	Joyita Dutta Richard M Leahy Quanzheng Li
author_sort	Joyita Dutta
title	Non-local means denoising of dynamic PET images.
title_short	Non-local means denoising of dynamic PET images.
title_full	Non-local means denoising of dynamic PET images.
title_fullStr	Non-local means denoising of dynamic PET images.
title_full_unstemmed	Non-local means denoising of dynamic PET images.
title_sort	non-local means denoising of dynamic pet images.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e3a72f2b090c403ba2963be15238c49a
work_keys_str_mv	AT joyitadutta nonlocalmeansdenoisingofdynamicpetimages AT richardmleahy nonlocalmeansdenoisingofdynamicpetimages AT quanzhengli nonlocalmeansdenoisingofdynamicpetimages
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Non-local means denoising of dynamic PET images.

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