Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data

The development of scanners with ultra-high gradient strength, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to...

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Autores principales:	C. Maffei, C. Lee, M. Planich, M. Ramprasad, N. Ravi, D. Trainor, Z. Urban, M. Kim, R.J. Jones, A. Henin, S.G. Hofmann, D.A. Pizzagalli, R.P. Auerbach, J.D.E. Gabrieli, S. Whitfield-Gabrieli, D.N. Greve, S.N. Haber, A. Yendiki
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Diffusion MRI Tractography White matter pathways Neuroanatomy Anatomical priors Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c298ff5501d24ad0a71f273b162e15e0
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spelling	oai:doaj.org-article:c298ff5501d24ad0a71f273b162e15e02021-11-18T04:44:57ZUsing diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data1095-957210.1016/j.neuroimage.2021.118706https://doaj.org/article/c298ff5501d24ad0a71f273b162e15e02021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009782https://doaj.org/toc/1095-9572The development of scanners with ultra-high gradient strength, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.C. MaffeiC. LeeM. PlanichM. RamprasadN. RaviD. TrainorZ. UrbanM. KimR.J. JonesA. HeninS.G. HofmannD.A. PizzagalliR.P. AuerbachJ.D.E. GabrieliS. Whitfield-GabrieliD.N. GreveS.N. HaberA. YendikiElsevierarticleDiffusion MRITractographyWhite matter pathwaysNeuroanatomyAnatomical priorsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118706- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diffusion MRI Tractography White matter pathways Neuroanatomy Anatomical priors Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Diffusion MRI Tractography White matter pathways Neuroanatomy Anatomical priors Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 C. Maffei C. Lee M. Planich M. Ramprasad N. Ravi D. Trainor Z. Urban M. Kim R.J. Jones A. Henin S.G. Hofmann D.A. Pizzagalli R.P. Auerbach J.D.E. Gabrieli S. Whitfield-Gabrieli D.N. Greve S.N. Haber A. Yendiki Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
description	The development of scanners with ultra-high gradient strength, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.
format	article
author	C. Maffei C. Lee M. Planich M. Ramprasad N. Ravi D. Trainor Z. Urban M. Kim R.J. Jones A. Henin S.G. Hofmann D.A. Pizzagalli R.P. Auerbach J.D.E. Gabrieli S. Whitfield-Gabrieli D.N. Greve S.N. Haber A. Yendiki
author_facet	C. Maffei C. Lee M. Planich M. Ramprasad N. Ravi D. Trainor Z. Urban M. Kim R.J. Jones A. Henin S.G. Hofmann D.A. Pizzagalli R.P. Auerbach J.D.E. Gabrieli S. Whitfield-Gabrieli D.N. Greve S.N. Haber A. Yendiki
author_sort	C. Maffei
title	Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
title_short	Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
title_full	Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
title_fullStr	Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
title_full_unstemmed	Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
title_sort	using diffusion mri data acquired with ultra-high gradient strength to improve tractography in routine-quality data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c298ff5501d24ad0a71f273b162e15e0
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Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data

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