Deep-learning based positron range correction of PET images

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating signif...

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Detalles Bibliográficos
Autores: López Herraiz, Joaquín, Bembibre, Adrián, López Montes, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/101228
Acceso en línea:https://hdl.handle.net/20.500.14352/101228
Access Level:acceso abierto
Palabra clave:539.1
53:61
Positron Range
Neural network
Positron Emission Tomography
Deep learning
Física nuclear
Programación de ordenadores (Física)
2207.20 Radioisótopos
3204.01 Medicina Nuclear
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oai_identifier_str oai:docta.ucm.es:20.500.14352/101228
network_acronym_str ES
network_name_str España
repository_id_str
spelling Deep-learning based positron range correction of PET imagesLópez Herraiz, JoaquínBembibre, AdriánLópez Montes, Alejandro539.153:61Positron RangeNeural networkPositron Emission TomographyDeep learningFísica nuclearProgramación de ordenadores (Física)2207.20 Radioisótopos3204.01 Medicina NuclearPositron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.MDPIUniversidad Complutense de Madrid20202020-01-0120202020-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/101228reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengB2017 BMD-3888 PRONTO-CM Not availableRTI2018-095800-A-I00 Not available Not availableNIH-R01-CA215700 Not available Not availableopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1012282026-06-02T12:44:21Z
dc.title.none.fl_str_mv Deep-learning based positron range correction of PET images
title Deep-learning based positron range correction of PET images
spellingShingle Deep-learning based positron range correction of PET images
López Herraiz, Joaquín
539.1
53:61
Positron Range
Neural network
Positron Emission Tomography
Deep learning
Física nuclear
Programación de ordenadores (Física)
2207.20 Radioisótopos
3204.01 Medicina Nuclear
title_short Deep-learning based positron range correction of PET images
title_full Deep-learning based positron range correction of PET images
title_fullStr Deep-learning based positron range correction of PET images
title_full_unstemmed Deep-learning based positron range correction of PET images
title_sort Deep-learning based positron range correction of PET images
dc.creator.none.fl_str_mv López Herraiz, Joaquín
Bembibre, Adrián
López Montes, Alejandro
author López Herraiz, Joaquín
author_facet López Herraiz, Joaquín
Bembibre, Adrián
López Montes, Alejandro
author_role author
author2 Bembibre, Adrián
López Montes, Alejandro
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 539.1
53:61
Positron Range
Neural network
Positron Emission Tomography
Deep learning
Física nuclear
Programación de ordenadores (Física)
2207.20 Radioisótopos
3204.01 Medicina Nuclear
topic 539.1
53:61
Positron Range
Neural network
Positron Emission Tomography
Deep learning
Física nuclear
Programación de ordenadores (Física)
2207.20 Radioisótopos
3204.01 Medicina Nuclear
description Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/101228
url https://hdl.handle.net/20.500.14352/101228
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv B2017 BMD-3888 PRONTO-CM Not available
RTI2018-095800-A-I00 Not available Not available
NIH-R01-CA215700 Not available Not available
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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