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- Callaghan, Martina F, et al.
(author)
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Example dataset for the hMRI toolbox
- 2019
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In: Data in Brief. - : Elsevier BV. - 2352-3409.
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Journal article (peer-reviewed)abstract
- The hMRI toolbox is an open-source toolbox for the calculation of quantitative MRI parameter maps from a series of weighted imaging data, and optionally additional calibration data. The multi-parameter mapping (MPM) protocol, incorporating calibration data to correct for spatial variation in the scanner’s transmit and receive fields, is the most complete protocol that can be handled by the toolbox. Here we present a dataset acquired with such a full MPM protocol, which is made freely available to be used as a tutorial by following instructions provided on the associated toolbox wiki pages, which can be found at http://hMRI.info, and following the theory described in: hMRI – A toolbox for quantitative MRI in neuroscience and clinical research.
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| 4. |
- Emmenegger, Tim M., et al.
(author)
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The Influence of Radio-Frequency Transmit Field Inhomogeneities on the Accuracy of G-ratio Weighted Imaging
- 2021
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In: Frontiers in Neuroscience. - : Frontiers Media SA. - 1662-4548 .- 1662-453X. ; 15
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Journal article (peer-reviewed)abstract
- G-ratio weighted imaging is a non-invasive, in-vivo MRI-based technique that aims at estimating an aggregated measure of relative myelination of axons across the entire brain white matter. The MR g-ratio and its constituents (axonal and myelin volume fraction) are more specific to the tissue microstructure than conventional MRI metrics targeting either the myelin or axonal compartment. To calculate the MR g-ratio, an MRI-based myelin-mapping technique is combined with an axon-sensitive MR technique (such as diffusion MRI). Correction for radio-frequency transmit (B1+) field inhomogeneities is crucial for myelin mapping techniques such as magnetization transfer saturation. Here we assessed the effect of B1+ correction on g-ratio weighted imaging. To this end, the B1+ field was measured and the B1+ corrected MR g-ratio was used as the reference in a Bland-Altman analysis. We found a substantial bias (≈-89%) and error (≈37%) relative to the dynamic range of g-ratio values in the white matter if the B1+ correction was not applied. Moreover, we tested the efficiency of a data-driven B1+ correction approach that was applied retrospectively without additional reference measurements. We found that it reduced the bias and error in the MR g-ratio by a factor of three. The data-driven correction is readily available in the open-source hMRI toolbox (www.hmri.info) which is embedded in the statistical parameter mapping (SPM) framework.
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- Fritz, Francisco J, et al.
(author)
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Effects of temperature in the estimation of inhomogeneous magnetic transfer (ihMT) in post-mortem human brain
- 2022
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In: Proceedings of the 2022 ISMRM & ISMRT Annual Meeting and Exhibition. ; 30
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Conference paper (peer-reviewed)abstract
- Inhomogeneous magnetic transfer (ihMT) is more sensitive to myelin macromolecules than standard MT proxies. Measuring ihMT in the multi-parameter mapping protocol allows calculating ihMT from MT saturation (MTsat) maps and thus inherently correct for the undesired dependencies on flip angle and the longitudinal relaxation rate. Further validation of this new ihMT metric requires measurement of MPM-based ihMT of human post-mortem material. Here, we showed that ihMT of a whole human post-mortem brain is feasible but can lead totemperature increase in the specimen, which is particularly pronounced in white matter.
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- Mohammadi, Siawoosh, et al.
(author)
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Characterising the temporal evolution of fixation in human post mortem brain via linear relaxometry modelling – a marker of cross-linking?
- 2019
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In: Characterising the temporal evolution of fixation in human post mortem brain via linear relaxometry modelling – a marker of cross-linking?. ; 27
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Conference paper (peer-reviewed)abstract
- MRI-based biophysical models are typically validated by comparison to ex-vivo histology of fixed tissue. The fixation process itself and the accompanied autolysis processes strongly modify tissue composition, and lead to MR signal changes, making the validation of biophysical models for in vivo MRI particularly challenging. To better understand the temporal evolution of the fixation process within the whole brain and its influence on MRI parameters, we monitor the temporal evolution of the fixation process of a whole human post-mortem brain using the linear relaxometry model across 15 time-points comprised of one unfixed, in-situ MRI scan and 14 ex-vivo MRI scans at different stages of the fixation process (days 1-93).
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- Mohammadi, Siawoosh, et al.
(author)
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From in situ to ex vivo: the effect of autolysis and fixation on quantitative MRI markers for myelin
- 2017
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In: ISMRM 25th Annual Meeting Proceedings. - 1545-4428. ; 25
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Conference paper (peer-reviewed)abstract
- Ex vivo histology remains the gold standard against which MRI biophysical models, e.g. the MR g-ratio which characterises the fraction of a fibre’s diameter that is myelinated, are evaluated. The MR g-ratio model requires a measure of myelin density, for which magnetization transfer saturation (MT) has been used as a biomarker. However, changes occurring post mortem, e.g. autolysis, temperature changes and fixation, significantly alter the MRI signal. Here we investigate how these changes impact MT. We found that MT decreased post mortem but greatlyincreased upon fixation. These effects are similar to reported changes of other established MRI myelin-markers.
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- Tabelow, Karsten, et al.
(author)
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hMRI – A toolbox for quantitative MRI in neuroscience and clinical research
- 2019
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In: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 194, s. 191-210
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Journal article (peer-reviewed)abstract
- Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
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| 10. |
- Weiskopf, Nikolaus, et al.
(author)
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Quantitative magnetic resonance imaging of brain anatomy and in vivo histology
- 2021
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In: Nature Reviews Physics. - : Springer Science and Business Media LLC. - 2522-5820.
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Research review (peer-reviewed)abstract
- Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which aims primarily at local image contrast. It provides specific physical parameters related to the nuclear spin of protons in water, such as relaxation times. These parameters carry information about the local microstructural environment of the protons (such as myelin in the brain). Non- invasive in vivo histology using MRI (hMRI) aims to use this information to directly characterize biological tissue microstructure, partially replacing or complementing classical invasive histology. The understanding of MRI tissue contrast provided by hMRI is, in turn, crucial for further improvements of qMRI, and they should be considered closely interlinked. We discuss concepts, models and validation approaches, pointing out challenges and the latest advances in this field. Further, we point out links to physics, including computational and analytical approaches and developments in materials science and photonics, that aid in reference data acquisition and model validation.
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