Literature DB >> 28230327

Fundamentals of diffusion MRI physics.

Valerij G Kiselev1.   

Abstract

Diffusion MRI is commonly considered the "engine" for probing the cellular structure of living biological tissues. The difficulty of this task is threefold. First, in structurally heterogeneous media, diffusion is related to structure in quite a complicated way. The challenge of finding diffusion metrics for a given structure is equivalent to other problems in physics that have been known for over a century. Second, in most cases the MRI signal is related to diffusion in an indirect way dependent on the measurement technique used. Third, finding the cellular structure given the MRI signal is an ill-posed inverse problem. This paper reviews well-established knowledge that forms the basis for responding to the first two challenges. The inverse problem is briefly discussed and the reader is warned about a number of pitfalls on the way.
Copyright © 2017 John Wiley & Sons, Ltd.

Mesh:

Year:  2017        PMID: 28230327     DOI: 10.1002/nbm.3602

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  22 in total

Review 1.  Transverse NMR relaxation in biological tissues.

Authors:  Valerij G Kiselev; Dmitry S Novikov
Journal:  Neuroimage       Date:  2018-06-07       Impact factor: 6.556

Review 2.  On modeling.

Authors:  Dmitry S Novikov; Valerij G Kiselev; Sune N Jespersen
Journal:  Magn Reson Med       Date:  2018-03-01       Impact factor: 4.668

3.  Motion-Induced Signal Loss in In Vivo Cardiac Diffusion-Weighted Imaging.

Authors:  Christian T Stoeck; Andrew D Scott; Pedro F Ferreira; Elizabeth M Tunnicliffe; Irvin Teh; Sonia Nielles-Vallespin; Kevin Moulin; David E Sosnovik; Magalie Viallon; Pierre Croisille; Sebastian Kozerke; David N Firmin; Daniel B Ennis; Jurgen E Schneider
Journal:  J Magn Reson Imaging       Date:  2019-04-29       Impact factor: 4.813

4.  What dominates the time dependence of diffusion transverse to axons: Intra- or extra-axonal water?

Authors:  Hong-Hsi Lee; Els Fieremans; Dmitry S Novikov
Journal:  Neuroimage       Date:  2017-12-16       Impact factor: 6.556

Review 5.  Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation.

Authors:  Dmitry S Novikov; Els Fieremans; Sune N Jespersen; Valerij G Kiselev
Journal:  NMR Biomed       Date:  2018-10-15       Impact factor: 4.044

6.  Design and validation of diffusion MRI models of white matter.

Authors:  Ileana O Jelescu; Matthew D Budde
Journal:  Front Phys       Date:  2017-11-28

7.  Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline.

Authors:  Benjamin Ades-Aron; Jelle Veraart; Peter Kochunov; Stephen McGuire; Paul Sherman; Elias Kellner; Dmitry S Novikov; Els Fieremans
Journal:  Neuroimage       Date:  2018-08-02       Impact factor: 6.556

8.  Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain.

Authors:  Vaibhav Mohanty; Emilie T McKinnon; Joseph A Helpern; Jens H Jensen
Journal:  Magn Reson Imaging       Date:  2018-01-03       Impact factor: 2.546

9.  Realistic Microstructure Simulator (RMS): Monte Carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images.

Authors:  Hong-Hsi Lee; Els Fieremans; Dmitry S Novikov
Journal:  J Neurosci Methods       Date:  2020-12-03       Impact factor: 2.390

Review 10.  The present and the future of microstructure MRI: From a paradigm shift to normal science.

Authors:  Dmitry S Novikov
Journal:  J Neurosci Methods       Date:  2020-10-21       Impact factor: 2.390

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