Literature DB >> 17353305

Diffusion MR imaging in multiple sclerosis: technical aspects and challenges.

E Pagani1, R Bammer, M A Horsfield, M Rovaris, A Gass, O Ciccarelli, M Filippi.   

Abstract

SUMMARY: Diffusion tensor (DT) MR imaging has frequently been applied in multiple sclerosis (MS) because of its ability to detect and quantify disease-related changes of the tissue microstructure within and outside T2-visible lesions. DT MR imaging data collection places high demands on scanner hardware and, though the acquisition and postprocessing can be relatively straightforward, numerous challenges remain in improving the reproducibility of this technique. Although there are some issues concerning image quality, echo-planar imaging is the most widely used acquisition scheme for diffusion imaging studies. Once the DT is estimated, indexes conveying the size, shape, and orientation of the DT can be calculated and further analyzed by using either histogram- or region-of-interest-based analyses. Because the orientation of the DT reflects the orientation of the axonal fibers of the brain, the pathways of the major white matter tracts can also be visualized. The DT model of diffusion, however, is not sufficient to characterize the diffusion properties of the brain when complex populations of fibers are present in a single voxel, and new ways to address this issue have been proposed. Two developments have enabled considerable improvements in the application of DT MR imaging: high magnetic field strengths and multicoil receiver arrays with parallel imaging. This review critically discusses models, acquisition, and postprocessing approaches that are currently available for DT MR imaging, as well as their limitations and possible improvements, to provide a better understanding of the strengths and weaknesses of this technique and a background for designing diffusion studies in MS.

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Year:  2007        PMID: 17353305      PMCID: PMC7977828     

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  77 in total

1.  Comparison of gradient encoding schemes for diffusion-tensor MRI.

Authors:  K M Hasan; D L Parker; A L Alexander
Journal:  J Magn Reson Imaging       Date:  2001-05       Impact factor: 4.813

2.  Noise correction for the exact determination of apparent diffusion coefficients at low SNR.

Authors:  O Dietrich; S Heiland; K Sartor
Journal:  Magn Reson Med       Date:  2001-03       Impact factor: 4.668

3.  Spatial transformations of diffusion tensor magnetic resonance images.

Authors:  D C Alexander; C Pierpaoli; P J Basser; J C Gee
Journal:  IEEE Trans Med Imaging       Date:  2001-11       Impact factor: 10.048

4.  7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images.

Authors:  J T Vaughan; M Garwood; C M Collins; W Liu; L DelaBarre; G Adriany; P Andersen; H Merkle; R Goebel; M B Smith; K Ugurbil
Journal:  Magn Reson Med       Date:  2001-07       Impact factor: 4.668

5.  Diffusion tensor imaging using single-shot SENSE-EPI.

Authors:  Roland Bammer; Martin Auer; Stephen L Keeling; Michael Augustin; Lara A Stables; Rupert W Prokesch; Rudolf Stollberger; Michael E Moseley; Franz Fazekas
Journal:  Magn Reson Med       Date:  2002-07       Impact factor: 4.668

6.  The longstanding MS lesion. A quantitative MRI and electron microscopic study.

Authors:  D Barnes; P M Munro; B D Youl; J W Prineas; W I McDonald
Journal:  Brain       Date:  1991-06       Impact factor: 13.501

7.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.

Authors:  J-Donald Tournier; Fernando Calamante; David G Gadian; Alan Connelly
Journal:  Neuroimage       Date:  2004-11       Impact factor: 6.556

Review 8.  Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases - a review.

Authors:  Mark A Horsfield; Derek K Jones
Journal:  NMR Biomed       Date:  2002 Nov-Dec       Impact factor: 4.044

9.  Correlation between magnetic resonance imaging findings and lesion development in chronic, active multiple sclerosis.

Authors:  D Katz; J K Taubenberger; B Cannella; D E McFarlin; C S Raine; H F McFarland
Journal:  Ann Neurol       Date:  1993-11       Impact factor: 10.422

10.  High field MRI correlates of myelin content and axonal density in multiple sclerosis--a post-mortem study of the spinal cord.

Authors:  J P Mottershead; K Schmierer; M Clemence; J S Thornton; F Scaravilli; G J Barker; P S Tofts; J Newcombe; M L Cuzner; R J Ordidge; W I McDonald; D H Miller
Journal:  J Neurol       Date:  2003-11       Impact factor: 4.849

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  22 in total

1.  Diffusion tensor imaging applications in multiple sclerosis patients using 3T magnetic resonance: a preliminary study.

Authors:  Lorenzo Testaverde; Laura Caporali; Eugenio Venditti; Giovanni Grillea; Claudio Colonnese
Journal:  Eur Radiol       Date:  2011-12-09       Impact factor: 5.315

Review 2.  Nonconventional MRI and microstructural cerebral changes in multiple sclerosis.

Authors:  Christian Enzinger; Frederik Barkhof; Olga Ciccarelli; Massimo Filippi; Ludwig Kappos; Maria A Rocca; Stefan Ropele; Àlex Rovira; Torben Schneider; Nicola de Stefano; Hugo Vrenken; Claudia Wheeler-Kingshott; Jens Wuerfel; Franz Fazekas
Journal:  Nat Rev Neurol       Date:  2015-11-03       Impact factor: 42.937

3.  The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity.

Authors:  Amy Kuceyeski; Jun Maruta; Norman Relkin; Ashish Raj
Journal:  Brain Connect       Date:  2013

4.  Magnetic resonance monitoring of lesion evolution in multiple sclerosis.

Authors:  Alex Rovira; Cristina Auger; Juli Alonso
Journal:  Ther Adv Neurol Disord       Date:  2013-09       Impact factor: 6.570

Review 5.  Magnetic Resonance Imaging in Multiple Sclerosis.

Authors:  Christopher C Hemond; Rohit Bakshi
Journal:  Cold Spring Harb Perspect Med       Date:  2018-05-01       Impact factor: 6.915

6.  Age-related changes in grey and white matter structure throughout adulthood.

Authors:  Antonio Giorgio; Luca Santelli; Valentina Tomassini; Rose Bosnell; Steve Smith; Nicola De Stefano; Heidi Johansen-Berg
Journal:  Neuroimage       Date:  2010-03-06       Impact factor: 6.556

7.  Automated vs. conventional tractography in multiple sclerosis: variability and correlation with disability.

Authors:  Daniel S Reich; Arzu Ozturk; Peter A Calabresi; Susumu Mori
Journal:  Neuroimage       Date:  2009-11-26       Impact factor: 6.556

Review 8.  High field MRI in the diagnosis of multiple sclerosis: high field-high yield?

Authors:  Mike P Wattjes; Frederik Barkhof
Journal:  Neuroradiology       Date:  2009-03-11       Impact factor: 2.804

9.  Clinical and conventional MRI predictors of disability and brain atrophy accumulation in RRMS. A large scale, short-term follow-up study.

Authors:  Sarlota Mesaros; Maria A Rocca; Maria P Sormani; Arnaud Charil; Giancarlo Comi; Massimo Filippi
Journal:  J Neurol       Date:  2008-07-03       Impact factor: 4.849

10.  Sex-specific extent and severity of white matter damage in multiple sclerosis: implications for cognitive decline.

Authors:  Menno M Schoonheim; René M Vigeveno; Fernanda C Rueda Lopes; Petra J W Pouwels; Chris H Polman; Frederik Barkhof; Jeroen J G Geurts
Journal:  Hum Brain Mapp       Date:  2013-08-24       Impact factor: 5.038

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