Quantitative MRI and Voxel-Based Quantification (VBQ)

Anatomical MR imaging has not only become a cornerstone in clinical diagnosis but also in neuroscience research. The great majority of anatomical studies rely on T1-weighted images for morphometric analysis of local gray matter volume using voxel-based morphometry (VBM). VBM provides insight into macroscopic volume changes that may highlight differences between groups; be associated with pathology or be indicative of plasticity. A complimentary approach that has sensitivity to tissue microstructure is high resolution quantitative imaging. Whereas in T1-weighted images the signal intensity is in arbitrary units and cannot be compared across sites or even scanning sessions, quantitative imaging can provide neuroimaging biomarkers for myelination, water and iron levels that are absolute measures comparable across imaging sites and time points.

 

The approach we have been developing is termed Multi-Parameter Mapping (MPM) [1] and provides whole-brain maps of relaxometry measures (R1 = 1/T1 and R2* = 1/T2*), magnetisation transfer saturation (MT) and effective proton density (PD*) with isotropic resolution of 1mm or higher. Example maps are shown below.

 

 

fig1

 

The MPM protocol produces maps of effective proton density (PD*), longitudinal relaxation rate (R1), magnetisation transfer saturation (MT) and effective transverse relaxation rate (R2*). Note that, unlike weighted images, these maps have particular units. These parameters serve as neuroimaging biomarkers for tissue microstructure.

 

To analyse the quantitative MPM data, we have also created a toolbox specifically for use with the MPM acquisition protocol. This Voxel-Based Quantification (VBQ) toolbox is available as a plug-in for SPM. The toolbox can generate maps and has routines specifically designed for group analysis whereby the quantitative data can be normalised to MNI space for statistical analysis in a means that preserves the quantitative values within a particular tissue class and, importantly, minimises partial volume effects. VBM classifies tissue types and measures anatomical shape, and VBQ provides complementary information through its sensitivity to tissue microstructure. This integrated approach to quantitative MRI and data analysis opens up new windows to studying the microanatomy of the human brain in vivo. As an example, recent work we have carried out has shown that the MPM data can detect age-related differences in tissue microstructure [2]:

 

fig2

 

Ageing related differences in quantitative parameters as revealed by VBQ show increases in R2* that are in line with increased iron concentration and reduction of MT and R1 that are in line with demyelination. See [2] for details

 

 

Some highlights from our ongoing methodological developments are:

  • In-vivo parcellation of the cortex at 800µm resolution or higher by using the multi-parameter maps as markers of myeloarchitecture, e.g. see Dick et al., 2012 and Sereno et al., 2013
  • The development of biophysical models to better understand the inter-dependence of these neuroimaging biomarkers as well as their dependence on key biological markers such as myelin and iron levels, which are normally only accessible through histological analysis, e.g. see Callaghan et al., 2014b
  • Using prospective motion correction to be more robust to volunteer movement during scanning. For this we have the first system installed in the UK by KinetiCor (www.kineticor.com). See below for an example.

 

With the MPM approach, particular emphasis is placed on short scan times, high precision and minimal bias in parameter estimates. Bias reduction is achieved by optimized RF transmit mapping using reference acquisitions [6] or post-processing methods [7]. Short scan times are not only efficient but also reduce sensitivity to motion, which will otherwise lead to artefacts in MR images. Another approach that we use to address motion artefact is prospective motion correction (PMC). This method utilises a camera placed in the bore of the magnet that tracks volunteer movement and updates the imaging system in real time. Below is an example of a quantitative map of R1 values created from data acquired with the volunteer moved throughout the acquisition.

 

fig2

 

When PMC is not used the maps have artefacts (left column) and structures are not well resolved but when PMC is used to track the movement image quality is greatly improved (right column) and structures become much more defined, e.g. the boundary of the corpus callosum, the cerebellum as well as individual sulci and gyri.

 

 

Primary contact

Martina Callaghan (m.callaghan «at» ucl.ac.uk)

 

References

[1]        Weiskopf N, Suckling J, Williams G, Correia MM, Inkster B, et al. (2013) Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front Neurosci 7: 111. Available here.

[2]        Callaghan MF, Freund P, Draganski B, Anderson E, Cappelletti M, et al. (2014) Wide-spread age-related differences in human brain microstructure revealed by quantitative MRI. Neurobiol Aging: 111. Available here.

[3]       Dick, F., Tierney, A.T., Lutti, A., Josephs, O., Sereno, M.I., Weiskopf, N., 2012. In vivo functional and myeloarchitectonic mapping of human primary auditory areas. J. Neurosci. 32, 16095105. Available here.

[4]       Sereno, M.I., Lutti, A., Weiskopf, N., Dick, F., 2013. Mapping the human cortical surface by combining quantitative T(1) with retinotopy. Cereb. Cortex 23, 22618. Available here.

[5]       Callaghan, M.F., Helms, G., Lutti, A., Mohammadi, S., Weiskopf, N., 2014b. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. Available here.

[6]       Lutti, A., Stadler, J., Josephs, O., Windischberger, C., Speck, O., Bernarding, J., Hutton, C., Weiskopf, N., 2012. Robust and Fast Whole Brain Mapping of the RF Transmit Field B1 at 7T. PLoS One 7, e32379. Available here.

[7]       Weiskopf, N., Lutti, A., Helms, G., Novak, M., Ashburner, J., Hutton, C., 2011. Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT). Neuroimage 54, 211624. Available here.

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