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.
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
Related authors
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Hong Jiang, Leo Svenningsson, and Daniel Topgaard
Magn. Reson., 4, 73–85, https://doi.org/10.5194/mr-4-73-2023, https://doi.org/10.5194/mr-4-73-2023, 2023
Short summary
Short summary
Diffusion MRI is a clinically important tool for noninvasive detection of pathological conditions that lead to microscopic changes in cell and tissue structures, but this technique suffers from interpretational ambiguities when applied to heterogeneous and disordered tissues comprising cells with multiple sizes, shapes, and orientations. We propose a simple scheme to encode the MRI signal with a sufficient amount of information to resolve and study all properties simultaneously.
Related subject area
Field: MRI | Topic: Signal processing methods
Lipid removal in deuterium metabolic imaging (DMI) using spatial prior knowledge
Nuclear spin noise tomography in three dimensions with iterative simultaneous algebraic reconstruction technique (SART) processing
Robin A. de Graaf, Yanning Liu, Zachary A. Corbin, and Henk M. De Feyter
Magn. Reson., 5, 21–31, https://doi.org/10.5194/mr-5-21-2024, https://doi.org/10.5194/mr-5-21-2024, 2024
Short summary
Short summary
Deuterium metabolic imaging (DMI) is a novel method to obtain images of dynamic metabolism in the living human brain. While DMI is generally simple and robust, small signals from deuterated skull lipids can distort the metabolic information within the brain. Here, we use MRI-based information on brain and skull locations to remove lipid signals from DMI data. With an average 90 % lipid removal, DMI is used to generate distinct and artifact-free metabolic maps on patients with brain tumors.
Stephan J. Ginthör, Judith Schlagnitweit, Matthias Bechmann, and Norbert Müller
Magn. Reson., 1, 165–173, https://doi.org/10.5194/mr-1-165-2020, https://doi.org/10.5194/mr-1-165-2020, 2020
Short summary
Short summary
For the first time, a three-dimensional map of the distribution of water in a test sample has been obtained from the random radio signal (spin noise) emitted spontaneously by hydrogen nuclei in a magnetic field with varying field gradients. A special variant of a projection–reconstruction algorithm has been developed for noise data, which allows one to adjust the image quality between high resolution/low contrast and low resolution/high contrast from the same previously recorded spin noise data.
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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...