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Isotropic diffusion weighting in PGSE NMR: Numerical optimization of the q-MAS PGSE sequence

Topgaard, Daniel LU (2013) In Microporous and Mesoporous Materials 178. p.60-63
Abstract
Isotropic diffusion weighting is employed in diffusion NMR and MRI for rapid determination of the trace of the diffusion tensor and for showing the presence of microscopic diffusion anisotropy in a globally isotropic material. In the recently introduced q-MAS PGSE sequence, short gradient pulses define the beginning and the end of the diffusion time by quickly increasing and reducing the magnitude of the q-vector, while isotropic diffusion weighting is achieved by low-amplitude harmonically modulated gradients that make the q-vector rotate at the magic angle from an axis fixed in the lab frame. While efficient and easily implemented on microimaging systems with high-gradient capabilities, the previous version of q-MAS PGSE is too demanding... (More)
Isotropic diffusion weighting is employed in diffusion NMR and MRI for rapid determination of the trace of the diffusion tensor and for showing the presence of microscopic diffusion anisotropy in a globally isotropic material. In the recently introduced q-MAS PGSE sequence, short gradient pulses define the beginning and the end of the diffusion time by quickly increasing and reducing the magnitude of the q-vector, while isotropic diffusion weighting is achieved by low-amplitude harmonically modulated gradients that make the q-vector rotate at the magic angle from an axis fixed in the lab frame. While efficient and easily implemented on microimaging systems with high-gradient capabilities, the previous version of q-MAS PGSE is too demanding for clinical MR scanners. Here, we present numerically optimized smooth gradient waveforms yielding maximum diffusion weighting for a given maximum gradient strength and echo time. (C) 2013 Elsevier Inc. All rights reserved. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
keywords
Magnetic resonance, Pulsed-gradient spin-echo, Magic-angle spinning, q-Vector, Microscopic diffusion anisotropy
in
Microporous and Mesoporous Materials
volume
178
pages
60 - 63
publisher
Elsevier
external identifiers
  • wos:000322426400016
  • scopus:84894901533
ISSN
1387-1811
DOI
10.1016/j.micromeso.2013.03.009
language
English
LU publication?
yes
id
49993283-7ff3-4c37-8eb7-595e607a79bd (old id 4042646)
date added to LUP
2013-10-07 16:42:58
date last changed
2019-07-16 02:05:04
@article{49993283-7ff3-4c37-8eb7-595e607a79bd,
  abstract     = {Isotropic diffusion weighting is employed in diffusion NMR and MRI for rapid determination of the trace of the diffusion tensor and for showing the presence of microscopic diffusion anisotropy in a globally isotropic material. In the recently introduced q-MAS PGSE sequence, short gradient pulses define the beginning and the end of the diffusion time by quickly increasing and reducing the magnitude of the q-vector, while isotropic diffusion weighting is achieved by low-amplitude harmonically modulated gradients that make the q-vector rotate at the magic angle from an axis fixed in the lab frame. While efficient and easily implemented on microimaging systems with high-gradient capabilities, the previous version of q-MAS PGSE is too demanding for clinical MR scanners. Here, we present numerically optimized smooth gradient waveforms yielding maximum diffusion weighting for a given maximum gradient strength and echo time. (C) 2013 Elsevier Inc. All rights reserved.},
  author       = {Topgaard, Daniel},
  issn         = {1387-1811},
  keyword      = {Magnetic resonance,Pulsed-gradient spin-echo,Magic-angle spinning,q-Vector,Microscopic diffusion anisotropy},
  language     = {eng},
  pages        = {60--63},
  publisher    = {Elsevier},
  series       = {Microporous and Mesoporous Materials},
  title        = {Isotropic diffusion weighting in PGSE NMR: Numerical optimization of the q-MAS PGSE sequence},
  url          = {http://dx.doi.org/10.1016/j.micromeso.2013.03.009},
  volume       = {178},
  year         = {2013},
}