| 1. |
|
|
| 2. |
|
|
| 3. |
- Callaghan, Martina F, et al.
(författare)
-
Example dataset for the hMRI toolbox
- 2019
-
Ingår i: Data in Brief. - : Elsevier BV. - 2352-3409.
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Helms, Gunther, et al.
(författare)
-
Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps
- 2009
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 47:1, s. 194-198
-
Tidskriftsartikel (refereegranskat)abstract
- Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.
|
|
| 8. |
|
|
| 9. |
- Tabelow, Karsten, et al.
(författare)
-
hMRI – A toolbox for quantitative MRI in neuroscience and clinical research
- 2019
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 194, s. 191-210
-
Tidskriftsartikel (refereegranskat)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.
|
|
