Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays

Autores
Caiafa, César Federico; Sporns, Olaf; Saykin, Andy; Pestilli, Franco
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFE_SD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados Unidos
Fil: Sporns, Olaf. Indiana University; Estados Unidos
Fil: Saykin, Andy. Indiana University; Estados Unidos
Fil: Pestilli, Franco. Indiana University; Estados Unidos
31st Conference on Neural Information Processing Systems
Long Beach
Estados Unidos
National Science Foundation
Materia
Multiway arrays
Diffusion Imaging
Tensor Decomposition
Tractography
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:11336/138582
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arraysCaiafa, César FedericoSporns, OlafSaykin, AndyPestilli, FrancoMultiway arraysDiffusion ImagingTensor DecompositionTractographyhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFE_SD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados UnidosFil: Sporns, Olaf. Indiana University; Estados UnidosFil: Saykin, Andy. Indiana University; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unidos31st Conference on Neural Information Processing SystemsLong BeachEstados UnidosNational Science FoundationNeural Information Processing Systems Foundation2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectConferenciaJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138582Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays; 31st Conference on Neural Information Processing Systems; Long Beach; Estados Unidos; 2017; 1-111738-2572CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://papers.nips.ccinfo:eu-repo/semantics/altIdentifier/url/https://par.nsf.gov/servlets/purl/10073354info:eu-repo/semantics/altIdentifier/url/https://proceedings.neurips.cc/paper/2017/hash/ccbd8ca962b80445df1f7f38c57759f0-Abstract.htmlInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:49:52Zoai:ri.conicet.gov.ar:11336/138582instacron: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:34982025-09-17 11:49:52.835CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
title Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
spellingShingle Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Caiafa, César Federico
Multiway arrays
Diffusion Imaging
Tensor Decomposition
Tractography
title_short Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
title_full Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
title_fullStr Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
title_full_unstemmed Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
title_sort Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
dc.creator.none.fl_str_mv Caiafa, César Federico
Sporns, Olaf
Saykin, Andy
Pestilli, Franco
author Caiafa, César Federico
author_facet Caiafa, César Federico
Sporns, Olaf
Saykin, Andy
Pestilli, Franco
author_role author
author2 Sporns, Olaf
Saykin, Andy
Pestilli, Franco
author2_role author
author
author
dc.subject.none.fl_str_mv Multiway arrays
Diffusion Imaging
Tensor Decomposition
Tractography
topic Multiway arrays
Diffusion Imaging
Tensor Decomposition
Tractography
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFE_SD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados Unidos
Fil: Sporns, Olaf. Indiana University; Estados Unidos
Fil: Saykin, Andy. Indiana University; Estados Unidos
Fil: Pestilli, Franco. Indiana University; Estados Unidos
31st Conference on Neural Information Processing Systems
Long Beach
Estados Unidos
National Science Foundation
description Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFE_SD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Conferencia
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/138582
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays; 31st Conference on Neural Information Processing Systems; Long Beach; Estados Unidos; 2017; 1-11
1738-2572
CONICET Digital
CONICET
url http://hdl.handle.net/11336/138582
identifier_str_mv Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays; 31st Conference on Neural Information Processing Systems; Long Beach; Estados Unidos; 2017; 1-11
1738-2572
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://papers.nips.cc
info:eu-repo/semantics/altIdentifier/url/https://par.nsf.gov/servlets/purl/10073354
info:eu-repo/semantics/altIdentifier/url/https://proceedings.neurips.cc/paper/2017/hash/ccbd8ca962b80445df1f7f38c57759f0-Abstract.html
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.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Neural Information Processing Systems Foundation
publisher.none.fl_str_mv Neural Information Processing Systems Foundation
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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score 13.001348

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