Articles | Volume 1, issue 1
https://doi.org/10.5194/mr-1-27-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/mr-1-27-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2020
Research article |
| 28 Feb 2020
| 28 Feb 2020
Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI
João P. de Almeida Martins
CORRESPONDING AUTHOR
Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
Random Walk Imaging AB, Lund, Sweden
Chantal M. W. Tax
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
Filip Szczepankiewicz
Harvard Medical School, Boston, MA, USA
Radiology, Brigham and Women's Hospital, Boston, MA, USA
Derek K. Jones
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
Mary MacKillop Institute for Health Research, Australian Catholic
University, Melbourne, Australia
Carl-Fredrik Westin
Harvard Medical School, Boston, MA, USA
Radiology, Brigham and Women's Hospital, Boston, MA, USA
Daniel Topgaard
Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
Random Walk Imaging AB, Lund, Sweden
Viewed
Total article views: 2,637 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Nov 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,783 | 781 | 73 | 2,637 | 351 | 74 | 68 |
- HTML: 1,783
- PDF: 781
- XML: 73
- Total: 2,637
- Supplement: 351
- BibTeX: 74
- EndNote: 68
Total article views: 2,159 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Feb 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,543 | 547 | 69 | 2,159 | 214 | 65 | 62 |
- HTML: 1,543
- PDF: 547
- XML: 69
- Total: 2,159
- Supplement: 214
- BibTeX: 65
- EndNote: 62
Total article views: 478 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Nov 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 240 | 234 | 4 | 478 | 137 | 9 | 6 |
- HTML: 240
- PDF: 234
- XML: 4
- Total: 478
- Supplement: 137
- BibTeX: 9
- EndNote: 6
Viewed (geographical distribution)
Total article views: 2,637 (including HTML, PDF, and XML)
Thereof 2,341 with geography defined
and 296 with unknown origin.
Total article views: 2,159 (including HTML, PDF, and XML)
Thereof 1,932 with geography defined
and 227 with unknown origin.
Total article views: 478 (including HTML, PDF, and XML)
Thereof 409 with geography defined
and 69 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
16 citations as recorded by crossref.
- Nonparametric 5D D-R2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain J. Rosenberg et al. 10.1016/j.jmr.2022.107256
- Matrix moments of the diffusion tensor distribution and matrix-variate Gamma approximation A. Reymbaut 10.1016/j.jmro.2021.100016
- Gradient waveform design for tensor-valued encoding in diffusion MRI F. Szczepankiewicz et al. 10.1016/j.jneumeth.2020.109007
- Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects P. Slator et al. 10.1002/mrm.28963
- Diffusion tensor distribution imaging of an in vivo mouse brain at ultrahigh magnetic field by spatiotemporal encoding M. Yon et al. 10.1002/nbm.4355
- Measuring compartmental T2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T2 correlation MRI C. Tax et al. 10.1016/j.neuroimage.2021.117967
- Nonparametric D-R1-R2 distribution MRI of the living human brain J. Martin et al. 10.1016/j.neuroimage.2021.118753
- Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation M. Barakovic et al. 10.1016/j.neuroimage.2020.117617
- Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain A. Reymbaut et al. 10.1002/mrm.28604
- Computing and visualising intra‐voxel orientation‐specific relaxation–diffusion features in the human brain J. de Almeida Martins et al. 10.1002/hbm.25224
- Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter J. de Almeida Martins et al. 10.1016/j.neuroimage.2021.118601
- Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact Q. Fan et al. 10.1016/j.neuroimage.2022.118958
- Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study I. Naranjo et al. 10.3390/cancers13071606
- Comparison of experimental times in T1-D and D-T2 correlation experiments in single-sided NMR E. Silletta et al. 10.1016/j.jmr.2021.107112
- Measurement of Full Diffusion Tensor Distribution Using High-Gradient Diffusion MRI and Applications in Diffuse Gliomas Y. Song et al. 10.3389/fphy.2022.813475
- Massively Multidimensional Diffusion-Relaxation Correlation MRI O. Narvaez et al. 10.3389/fphy.2021.793966
16 citations as recorded by crossref.
- Nonparametric 5D D-R2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain J. Rosenberg et al. 10.1016/j.jmr.2022.107256
- Matrix moments of the diffusion tensor distribution and matrix-variate Gamma approximation A. Reymbaut 10.1016/j.jmro.2021.100016
- Gradient waveform design for tensor-valued encoding in diffusion MRI F. Szczepankiewicz et al. 10.1016/j.jneumeth.2020.109007
- Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects P. Slator et al. 10.1002/mrm.28963
- Diffusion tensor distribution imaging of an in vivo mouse brain at ultrahigh magnetic field by spatiotemporal encoding M. Yon et al. 10.1002/nbm.4355
- Measuring compartmental T2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T2 correlation MRI C. Tax et al. 10.1016/j.neuroimage.2021.117967
- Nonparametric D-R1-R2 distribution MRI of the living human brain J. Martin et al. 10.1016/j.neuroimage.2021.118753
- Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation M. Barakovic et al. 10.1016/j.neuroimage.2020.117617
- Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain A. Reymbaut et al. 10.1002/mrm.28604
- Computing and visualising intra‐voxel orientation‐specific relaxation–diffusion features in the human brain J. de Almeida Martins et al. 10.1002/hbm.25224
- Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter J. de Almeida Martins et al. 10.1016/j.neuroimage.2021.118601
- Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact Q. Fan et al. 10.1016/j.neuroimage.2022.118958
- Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study I. Naranjo et al. 10.3390/cancers13071606
- Comparison of experimental times in T1-D and D-T2 correlation experiments in single-sided NMR E. Silletta et al. 10.1016/j.jmr.2021.107112
- Measurement of Full Diffusion Tensor Distribution Using High-Gradient Diffusion MRI and Applications in Diffuse Gliomas Y. Song et al. 10.3389/fphy.2022.813475
- Massively Multidimensional Diffusion-Relaxation Correlation MRI O. Narvaez et al. 10.3389/fphy.2021.793966
Latest update: 25 Apr 2024
Short summary
Detailed interpretation of brain MRI data is hampered by the fact that each imaging voxel comprises several types of cells and tissues. To address this, we adapt signal encoding and data inversion strategies from solid-state and low-field NMR to quantify the sub-voxel heterogeneity of the human brain with 5D relaxation–diffusion distributions wherein distinct tissue components are resolved, individually characterized, and subsequently mapped throughout the volume of the brain.
Detailed interpretation of brain MRI data is hampered by the fact that each imaging voxel...
