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Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI
The foundation for an accurate and unifying Fourier-based theory of diffusion weighted magnetic resonance imaging (DW–MRI) is constructed by carefully re-examining the first principles of DW–MRI signal formation and deriving its mathematical model from scratch. The derivations are specifically obtai...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2013
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613761/ https://www.ncbi.nlm.nih.gov/pubmed/23596401 http://dx.doi.org/10.3389/fnint.2013.00018 |
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author | Özcan, Alpay |
author_facet | Özcan, Alpay |
author_sort | Özcan, Alpay |
collection | PubMed |
description | The foundation for an accurate and unifying Fourier-based theory of diffusion weighted magnetic resonance imaging (DW–MRI) is constructed by carefully re-examining the first principles of DW–MRI signal formation and deriving its mathematical model from scratch. The derivations are specifically obtained for DW–MRI signal by including all of its elements (e.g., imaging gradients) using complex values. Particle methods are utilized in contrast to conventional partial differential equations approach. The signal is shown to be the Fourier transform of the joint distribution of number of the magnetic moments (at a given location at the initial time) and magnetic moment displacement integrals. In effect, the k-space is augmented by three more dimensions, corresponding to the frequency variables dual to displacement integral vectors. The joint distribution function is recovered by applying the Fourier transform to the complete high-dimensional data set. In the process, to obtain a physically meaningful real valued distribution function, phase corrections are applied for the re-establishment of Hermitian symmetry in the signal. Consequently, the method is fully unconstrained and directly presents the distribution of displacement integrals without any assumptions such as symmetry or Markovian property. The joint distribution function is visualized with isosurfaces, which describe the displacement integrals, overlaid on the distribution map of the number of magnetic moments with low mobility. The model provides an accurate description of the molecular motion measurements via DW–MRI. The improvement of the characterization of tissue microstructure leads to a better localization, detection and assessment of biological properties such as white matter integrity. The results are demonstrated on the experimental data obtained from an ex vivo baboon brain. |
format | Online Article Text |
id | pubmed-3613761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-36137612013-04-17 Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI Özcan, Alpay Front Integr Neurosci Neuroscience The foundation for an accurate and unifying Fourier-based theory of diffusion weighted magnetic resonance imaging (DW–MRI) is constructed by carefully re-examining the first principles of DW–MRI signal formation and deriving its mathematical model from scratch. The derivations are specifically obtained for DW–MRI signal by including all of its elements (e.g., imaging gradients) using complex values. Particle methods are utilized in contrast to conventional partial differential equations approach. The signal is shown to be the Fourier transform of the joint distribution of number of the magnetic moments (at a given location at the initial time) and magnetic moment displacement integrals. In effect, the k-space is augmented by three more dimensions, corresponding to the frequency variables dual to displacement integral vectors. The joint distribution function is recovered by applying the Fourier transform to the complete high-dimensional data set. In the process, to obtain a physically meaningful real valued distribution function, phase corrections are applied for the re-establishment of Hermitian symmetry in the signal. Consequently, the method is fully unconstrained and directly presents the distribution of displacement integrals without any assumptions such as symmetry or Markovian property. The joint distribution function is visualized with isosurfaces, which describe the displacement integrals, overlaid on the distribution map of the number of magnetic moments with low mobility. The model provides an accurate description of the molecular motion measurements via DW–MRI. The improvement of the characterization of tissue microstructure leads to a better localization, detection and assessment of biological properties such as white matter integrity. The results are demonstrated on the experimental data obtained from an ex vivo baboon brain. Frontiers Media S.A. 2013-04-02 /pmc/articles/PMC3613761/ /pubmed/23596401 http://dx.doi.org/10.3389/fnint.2013.00018 Text en Copyright © 2013 Özcan. http://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Neuroscience Özcan, Alpay Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title | Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title_full | Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title_fullStr | Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title_full_unstemmed | Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title_short | Complete fourier direct magnetic resonance imaging (CFD-MRI) for diffusion MRI |
title_sort | complete fourier direct magnetic resonance imaging (cfd-mri) for diffusion mri |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613761/ https://www.ncbi.nlm.nih.gov/pubmed/23596401 http://dx.doi.org/10.3389/fnint.2013.00018 |
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