A hitchhiker's guide to diffusion tensor imaging

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Abstract

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.

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Most cited references 124

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Klaas Pruessmann, Markus Weiger, Markus Scheidegger … (1999)

New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two-coil array in brain imaging. With an array of five coils double-oblique heart images were obtained in one-third of conventional scan time. Magn Reson Med 42:952-962, 1999. Copyright 1999 Wiley-Liss, Inc.

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Principles of diffusion tensor imaging and its applications to basic neuroscience research.

Susumu Mori, Jiangyang Zhang (2006)

The brain contains more than 100 billion neurons that communicate with each other via axons for the formation of complex neural networks. The structural mapping of such networks during health and disease states is essential for understanding brain function. However, our understanding of brain structural connectivity is surprisingly limited, due in part to the lack of noninvasive methodologies to study axonal anatomy. Diffusion tensor imaging (DTI) is a recently developed MRI technique that can measure macroscopic axonal organization in nervous system tissues. In this article, the principles of DTI methodologies are explained, and several applications introduced, including visualization of axonal tracts in myelin and axonal injuries as well as human brain and mouse embryonic development. The strengths and limitations of DTI and key areas for future research and development are also discussed.

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Martijn P. van den Heuvel, René C.W. Mandl, René S Kahn … (2009)

During rest, multiple cortical brain regions are functionally linked forming resting-state networks. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffusion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

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Author and article information

Journal

Journal ID (nlm-ta): Front Neurosci

Journal ID (iso-abbrev): Front Neurosci

Journal ID (publisher-id): Front. Neurosci.

Title: Frontiers in Neuroscience

Publisher: Frontiers Media S.A.

ISSN (Print): 1662-4548

ISSN (Electronic): 1662-453X

Publication date (Electronic preprint): 22 December 2012

Publication date (Electronic): 12 March 2013

Publication date Collection: 2013

Volume: 7

Electronic Location Identifier: 31

Affiliations

[1] ¹Life and Health Science Research Institute (ICVS), School of Health Sciences, University of Minho Braga, Portugal

[2] ²ICVS/3B's - PT Government Associate Laboratory Braga/Guimarães, Portugal

[3] ³Department of Informatics, University of Minho Braga, Portugal

Author notes

Edited by: Arno Klein, Cornell Medical School, USA

Reviewed by: Arno Klein, Cornell Medical School, USA; Eleftherios Garyfallidis, University of Cambridge, UK; Mahshid Farzinfar, University of North Carolina at Chapel Hill, USA

*Correspondence: José M. Soares, Life and Health Science Research Institute (ICVS), School of Health Sciences, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal. e-mail: josesoares@ 123456ecsaude.uminho.pt

This article was submitted to Frontiers in Brain Imaging Methods, a specialty of Frontiers in Neuroscience.

Article

DOI: 10.3389/fnins.2013.00031

PMC ID: 3594764

PubMed ID: 23486659

SO-VID: 22655453-3fbc-49ac-af9b-9ec21ae10e6d

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

History

Date received : 09 November 2012

Date accepted : 23 February 2013

Page count

Figures: 1, Tables: 2, Equations: 2, References: 205, Pages: 14, Words: 13278

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