Influence of ultrasound speckle tracking strategies for motion and strain estimation

Autores
Curiale, Ariel Hernán; Vegas Sánchez Ferrero, Gonzalo; Aja Fernández, Santiago
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.
Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Fundación Escuela Medicina Nuclear; Argentina. Universidad de Valladolid; España
Fil: Vegas Sánchez Ferrero, Gonzalo. Harvard Medical School; Estados Unidos. Universidad Politécnica de Madrid; España
Fil: Aja Fernández, Santiago. Universidad de Valladolid; España
Materia
Demons Registration
Diffeomorphic Registration
Echocardiography
Generalized Gamma
Local Correlation
Maximum Likelihood
Mixture Model
Optical Flow
Speckle Model
Speckle Tracking
Strain Estimation
Ultrasound Images
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/59674

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spelling Influence of ultrasound speckle tracking strategies for motion and strain estimationCuriale, Ariel HernánVegas Sánchez Ferrero, GonzaloAja Fernández, SantiagoDemons RegistrationDiffeomorphic RegistrationEchocardiographyGeneralized GammaLocal CorrelationMaximum LikelihoodMixture ModelOptical FlowSpeckle ModelSpeckle TrackingStrain EstimationUltrasound Imageshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Fundación Escuela Medicina Nuclear; Argentina. Universidad de Valladolid; EspañaFil: Vegas Sánchez Ferrero, Gonzalo. Harvard Medical School; Estados Unidos. Universidad Politécnica de Madrid; EspañaFil: Aja Fernández, Santiago. Universidad de Valladolid; EspañaElsevier Science2016-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59674Curiale, Ariel Hernán; Vegas Sánchez Ferrero, Gonzalo; Aja Fernández, Santiago; Influence of ultrasound speckle tracking strategies for motion and strain estimation; Elsevier Science; Medical Image Analysis; 32; 8-2016; 184-2001361-8415CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.media.2016.04.002info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1361841516300202info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:14Zoai:ri.conicet.gov.ar:11336/59674instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:15.25CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Influence of ultrasound speckle tracking strategies for motion and strain estimation
title Influence of ultrasound speckle tracking strategies for motion and strain estimation
spellingShingle Influence of ultrasound speckle tracking strategies for motion and strain estimation
Curiale, Ariel Hernán
Demons Registration
Diffeomorphic Registration
Echocardiography
Generalized Gamma
Local Correlation
Maximum Likelihood
Mixture Model
Optical Flow
Speckle Model
Speckle Tracking
Strain Estimation
Ultrasound Images
title_short Influence of ultrasound speckle tracking strategies for motion and strain estimation
title_full Influence of ultrasound speckle tracking strategies for motion and strain estimation
title_fullStr Influence of ultrasound speckle tracking strategies for motion and strain estimation
title_full_unstemmed Influence of ultrasound speckle tracking strategies for motion and strain estimation
title_sort Influence of ultrasound speckle tracking strategies for motion and strain estimation
dc.creator.none.fl_str_mv Curiale, Ariel Hernán
Vegas Sánchez Ferrero, Gonzalo
Aja Fernández, Santiago
author Curiale, Ariel Hernán
author_facet Curiale, Ariel Hernán
Vegas Sánchez Ferrero, Gonzalo
Aja Fernández, Santiago
author_role author
author2 Vegas Sánchez Ferrero, Gonzalo
Aja Fernández, Santiago
author2_role author
author
dc.subject.none.fl_str_mv Demons Registration
Diffeomorphic Registration
Echocardiography
Generalized Gamma
Local Correlation
Maximum Likelihood
Mixture Model
Optical Flow
Speckle Model
Speckle Tracking
Strain Estimation
Ultrasound Images
topic Demons Registration
Diffeomorphic Registration
Echocardiography
Generalized Gamma
Local Correlation
Maximum Likelihood
Mixture Model
Optical Flow
Speckle Model
Speckle Tracking
Strain Estimation
Ultrasound Images
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.
Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Fundación Escuela Medicina Nuclear; Argentina. Universidad de Valladolid; España
Fil: Vegas Sánchez Ferrero, Gonzalo. Harvard Medical School; Estados Unidos. Universidad Politécnica de Madrid; España
Fil: Aja Fernández, Santiago. Universidad de Valladolid; España
description Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.
publishDate 2016
dc.date.none.fl_str_mv 2016-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/59674
Curiale, Ariel Hernán; Vegas Sánchez Ferrero, Gonzalo; Aja Fernández, Santiago; Influence of ultrasound speckle tracking strategies for motion and strain estimation; Elsevier Science; Medical Image Analysis; 32; 8-2016; 184-200
1361-8415
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59674
identifier_str_mv Curiale, Ariel Hernán; Vegas Sánchez Ferrero, Gonzalo; Aja Fernández, Santiago; Influence of ultrasound speckle tracking strategies for motion and strain estimation; Elsevier Science; Medical Image Analysis; 32; 8-2016; 184-200
1361-8415
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.media.2016.04.002
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1361841516300202
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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