schliessen

Filtern

 

Bibliotheken

Track-weighted imaging methods: extracting information from a streamlines tractogram

A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information fro... Full description

Journal Title: Magnetic Resonance Materials in Physics Biology and Medicine, 2017, Vol.30(4), pp.317-335
Main Author: Calamante, Fernando
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0968-5243 ; E-ISSN: 1352-8661 ; DOI: 10.1007/s10334-017-0608-1
Link: http://dx.doi.org/10.1007/s10334-017-0608-1
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: springer_jour10.1007/s10334-017-0608-1
title: Track-weighted imaging methods: extracting information from a streamlines tractogram
format: Article
creator:
  • Calamante, Fernando
subjects:
  • Fibre-tracking
  • Tractogram
  • Super-resolution
  • Connectivity
  • Tractography
ispartof: Magnetic Resonance Materials in Physics, Biology and Medicine, 2017, Vol.30(4), pp.317-335
description: A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T 2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
language: eng
source:
identifier: ISSN: 0968-5243 ; E-ISSN: 1352-8661 ; DOI: 10.1007/s10334-017-0608-1
fulltext: fulltext
issn:
  • 1352-8661
  • 13528661
  • 0968-5243
  • 09685243
url: Link


@attributes
ID1487609966
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid10.1007/s10334-017-0608-1
sourceidspringer_jour
recordidTN_springer_jour10.1007/s10334-017-0608-1
sourcesystemOther
pqid1866695603
galeid499186064
display
typearticle
titleTrack-weighted imaging methods: extracting information from a streamlines tractogram
creatorCalamante, Fernando
ispartofMagnetic Resonance Materials in Physics, Biology and Medicine, 2017, Vol.30(4), pp.317-335
identifier
subjectFibre-tracking ; Tractogram ; Super-resolution ; Connectivity ; Tractography
descriptionA whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T 2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
languageeng
source
version5
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
backlink$$Uhttp://dx.doi.org/10.1007/s10334-017-0608-1$$EView_full_text_in_Springer_(Subscribers_only)
search
creatorcontribCalamante, Fernando
titleTrack-weighted imaging methods: extracting information from a streamlines tractogram
descriptionA whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T 2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
subject
0Fibre-tracking
1Tractogram
2Super-resolution
3Connectivity
4Tractography
general
010.1007/s10334-017-0608-1
1English
2Springer Science & Business Media B.V.
3SpringerLink
sourceidspringer_jour
recordidspringer_jour10.1007/s10334-017-0608-1
issn
01352-8661
113528661
20968-5243
309685243
rsrctypearticle
creationdate2017
addtitle
0Magnetic Resonance Materials in Physics, Biology and Medicine
1Official Journal of the European Society for Magnetic Resonance in Medicine and Biology
2Magn Reson Mater Phy
searchscopespringer_journals_complete
scopespringer_journals_complete
lsr30VSR-Enriched:[pqid, pages, galeid, orcidid]
sort
titleTrack-weighted imaging methods: extracting information from a streamlines tractogram
authorCalamante, Fernando
creationdate20170800
facets
frbrgroupid6613869959082581704
frbrtype5
newrecords20170801
languageeng
creationdate2017
topic
0Fibre-Tracking
1Tractogram
2Super-Resolution
3Connectivity
4Tractography
collectionSpringerLink
prefilterarticles
rsrctypearticles
creatorcontribCalamante, Fernando
jtitleMagnetic Resonance Materials In Physics, Biology And Medicine
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
aulastCalamante
aufirstFernando
auCalamante, Fernando
atitleTrack-weighted imaging methods: extracting information from a streamlines tractogram
jtitleMagnetic Resonance Materials in Physics, Biology and Medicine
stitleMagn Reson Mater Phy
addtitleOfficial Journal of the European Society for Magnetic Resonance in Medicine and Biology
risdate201708
volume30
issue4
spage317
epage335
issn0968-5243
eissn1352-8661
genrearticle
ristypeJOUR
abstractA whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T 2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
copBerlin/Heidelberg
pubSpringer Berlin Heidelberg
doi10.1007/s10334-017-0608-1
pages317-335
orcidid0000-0002-7550-3142
date2017-08