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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/138582
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T10:09:10Zoai: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:34982025-09-03 10:09:11.217CONICET 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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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Internacional |
dc.publisher.none.fl_str_mv |
Neural Information Processing Systems Foundation |
publisher.none.fl_str_mv |
Neural Information Processing Systems Foundation |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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