Time-optimized high-resolution readout-segmented diffusion tensor imaging.

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. There...

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Autores principales:	Gernot Reishofer, Karl Koschutnig, Christian Langkammer, David Porter, Margit Jehna, Christian Enzinger, Stephen Keeling, Franz Ebner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04ae810a37134f3fa1e7c8127c881338
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id	oai:doaj.org-article:04ae810a37134f3fa1e7c8127c881338
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spelling	oai:doaj.org-article:04ae810a37134f3fa1e7c8127c8813382021-11-18T08:57:09ZTime-optimized high-resolution readout-segmented diffusion tensor imaging.1932-620310.1371/journal.pone.0074156https://doaj.org/article/04ae810a37134f3fa1e7c8127c8813382013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24019951/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1 × 1 × 2.5 mm(3)) diffusion tensor imaging of the entire brain applicable in a clinical context.Gernot ReishoferKarl KoschutnigChristian LangkammerDavid PorterMargit JehnaChristian EnzingerStephen KeelingFranz EbnerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e74156 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gernot Reishofer Karl Koschutnig Christian Langkammer David Porter Margit Jehna Christian Enzinger Stephen Keeling Franz Ebner Time-optimized high-resolution readout-segmented diffusion tensor imaging.
description	Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1 × 1 × 2.5 mm(3)) diffusion tensor imaging of the entire brain applicable in a clinical context.
format	article
author	Gernot Reishofer Karl Koschutnig Christian Langkammer David Porter Margit Jehna Christian Enzinger Stephen Keeling Franz Ebner
author_facet	Gernot Reishofer Karl Koschutnig Christian Langkammer David Porter Margit Jehna Christian Enzinger Stephen Keeling Franz Ebner
author_sort	Gernot Reishofer
title	Time-optimized high-resolution readout-segmented diffusion tensor imaging.
title_short	Time-optimized high-resolution readout-segmented diffusion tensor imaging.
title_full	Time-optimized high-resolution readout-segmented diffusion tensor imaging.
title_fullStr	Time-optimized high-resolution readout-segmented diffusion tensor imaging.
title_full_unstemmed	Time-optimized high-resolution readout-segmented diffusion tensor imaging.
title_sort	time-optimized high-resolution readout-segmented diffusion tensor imaging.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/04ae810a37134f3fa1e7c8127c881338
work_keys_str_mv	AT gernotreishofer timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT karlkoschutnig timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT christianlangkammer timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT davidporter timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT margitjehna timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT christianenzinger timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT stephenkeeling timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging AT franzebner timeoptimizedhighresolutionreadoutsegmenteddiffusiontensorimaging
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Time-optimized high-resolution readout-segmented diffusion tensor imaging.

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