Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature

Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images....

ver descrição completa

Detalhes bibliográficos
Autores: Boris Jesús Mederos Madrazo, Jie Zhao, Leticia Ortega-Maynez, Nelly Gordillo Castillo, jose mejia
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:México
Recursos:Universidad Autónoma de Ciudad Juárez
Repositorio:Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez
OAI Identifier:oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-4537
Acesso em linha:https://doi.org/10.1155/2018/4706165
Access Level:acceso abierto
Palavra-chave:Reconstruction
gaussian curvature
info:eu-repo/classification/cti/7
id MX_c06a5b9256f1d91ef9cc7cd8e086ab8e
oai_identifier_str oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-4537
network_acronym_str MX
network_name_str México
repository_id_str
spelling Reconstruction of Positron Emission Tomography Images Using Gaussian CurvatureBoris Jesús Mederos MadrazoJie ZhaoLeticia Ortega-MaynezNelly Gordillo Castillojose mejiaReconstructiongaussian curvatureinfo:eu-repo/classification/cti/7Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images. In this paper, we proposed a reconstruction algorithm to improve the image reconstruction process, addressing the problem from a variational geometric perspective. We proposed using the weighted Gaussian curvature (WGC) as a regularization term to better deal with noise and preserve the original geometry of the image, such as the lesion structure. In other contexts, the WGC term has been found to have excellent capabilities for preserving borders and structures of low gradient magnitude, such as ramp-like structures; at the same time, it effectively removes noise in the image. We presented several experiments aimed at evaluating contrast and lesion detectability in the reconstructed images. The results for simulated images and real data showed that our proposed algorithm effectively preserves lesions and removes noise.Boris Jesús Mederos MadrazoJie ZhaoLeticia Ortega MaynezNelly Gordillo Castillo2018info:eu-repo/semantics/articleArtículoinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1155/2018/4706165reponame:Repositorio Institucional de la Universidad Autónoma de Ciudad Juárezinstname:Universidad Autónoma de Ciudad Juárezinstacron:UACJen_USinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0oai:uacj.mx:oai:cathi.uacj.mx:20.500.11961ir-45372025-11-26T19:47:26Z
dc.title.none.fl_str_mv Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
title Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
spellingShingle Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
Boris Jesús Mederos Madrazo
Reconstruction
gaussian curvature
info:eu-repo/classification/cti/7
title_short Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
title_full Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
title_fullStr Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
title_full_unstemmed Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
title_sort Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature
dc.creator.none.fl_str_mv Boris Jesús Mederos Madrazo
Jie Zhao
Leticia Ortega-Maynez
Nelly Gordillo Castillo
jose mejia
author Boris Jesús Mederos Madrazo
author_facet Boris Jesús Mederos Madrazo
Jie Zhao
Leticia Ortega-Maynez
Nelly Gordillo Castillo
jose mejia
author_role author
author2 Jie Zhao
Leticia Ortega-Maynez
Nelly Gordillo Castillo
jose mejia
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Boris Jesús Mederos Madrazo
Jie Zhao
Leticia Ortega Maynez
Nelly Gordillo Castillo
dc.subject.none.fl_str_mv Reconstruction
gaussian curvature
info:eu-repo/classification/cti/7
topic Reconstruction
gaussian curvature
info:eu-repo/classification/cti/7
description Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images. In this paper, we proposed a reconstruction algorithm to improve the image reconstruction process, addressing the problem from a variational geometric perspective. We proposed using the weighted Gaussian curvature (WGC) as a regularization term to better deal with noise and preserve the original geometry of the image, such as the lesion structure. In other contexts, the WGC term has been found to have excellent capabilities for preserving borders and structures of low gradient magnitude, such as ramp-like structures; at the same time, it effectively removes noise in the image. We presented several experiments aimed at evaluating contrast and lesion detectability in the reconstructed images. The results for simulated images and real data showed that our proposed algorithm effectively preserves lesions and removes noise.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
Artículo
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1155/2018/4706165
url https://doi.org/10.1155/2018/4706165
dc.language.none.fl_str_mv en_US
language_invalid_str_mv en_US
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez
instname:Universidad Autónoma de Ciudad Juárez
instacron:UACJ
instname_str Universidad Autónoma de Ciudad Juárez
instacron_str UACJ
institution UACJ
reponame_str Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez
collection Repositorio Institucional de la Universidad Autónoma de Ciudad Juárez
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1858176818495881216
score 15,811543

Registos relacionados