Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI

Abstract Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinic...

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Autores principales:	Siying Wang, Christian Ledig, Joseph V. Hajnal, Serena J. Counsell, Julia A. Schnabel, Maria Deprez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0e382f0943f4bea92a56d612cda81e1
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spelling	oai:doaj.org-article:c0e382f0943f4bea92a56d612cda81e12021-12-02T16:08:28ZQuantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI10.1038/s41598-019-49350-32045-2322https://doaj.org/article/c0e382f0943f4bea92a56d612cda81e12019-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-49350-3https://doaj.org/toc/2045-2322Abstract Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.Siying WangChristian LedigJoseph V. HajnalSerena J. CounsellJulia A. SchnabelMaria DeprezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Siying Wang Christian Ledig Joseph V. Hajnal Serena J. Counsell Julia A. Schnabel Maria Deprez Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
description	Abstract Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.
format	article
author	Siying Wang Christian Ledig Joseph V. Hajnal Serena J. Counsell Julia A. Schnabel Maria Deprez
author_facet	Siying Wang Christian Ledig Joseph V. Hajnal Serena J. Counsell Julia A. Schnabel Maria Deprez
author_sort	Siying Wang
title	Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
title_short	Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
title_full	Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
title_fullStr	Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
title_full_unstemmed	Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI
title_sort	quantitative assessment of myelination patterns in preterm neonates using t2-weighted mri
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/c0e382f0943f4bea92a56d612cda81e1
work_keys_str_mv	AT siyingwang quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri AT christianledig quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri AT josephvhajnal quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri AT serenajcounsell quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri AT juliaaschnabel quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri AT mariadeprez quantitativeassessmentofmyelinationpatternsinpretermneonatesusingt2weightedmri
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Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI

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