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 morphometry, such as measurements of local gray matter volume using voxel-based morphometry (VBM). However, the neuroanatomical interpretation of this approach is limited, since T1w images mix different contrast mechanisms and their signal amplitude is not quantitative, i.e., signal intensities cannot be compared between different sites and changes in signal may originate from different underlying causes. We are developing new approaches to anatomical imaging based on quantitative multi-parameter mapping that allows for fast whole brain mapping of T1, T2*, proton density (PD) and magnetization transfer (MT) with at least 1 mm resolution. These parameters are sensitive to iron concentration, water content and myelination.


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 [1] or post-processing methods (UNICORT; [2]).


MT maps show improved contrast in subcortical areas, yielding improved VBM results (Fig. 1; [3]). The quantitative imaging approach is complemented by the introduction of voxel-based quantification (VBQ), which allows for whole brain assessment of the parameter values [4]. While VBM classifies tissue types and measures anatomical shape, VBQ complements it through its sensitivity to tissue microstructure indirectly reflected by the different parameter values. This novel integrated approach of quantitative MRI and data analysis approaches opens up new windows to studying the microanatomy of the human brain in vivo.




Fig 1: Increased contrast of MT parameter maps compared to standard T1w MDEFT images improves assessment of subcortical brain regions (modified from [3]).




Fig 2: Ageing related changes in local gray matter volume as indicated by VBM (left colum) and quantitative parameters as revealed by VBQ (center and right colum). Increases in R2* with age are in line with increased iron concentration and reduction of MT with age are in line with changes in myelination. See [4] for details



Current applications include the study of normal ageing [4], developmental prosopagnosia [5], and neurodegenerative disease. Ongoing methodological developments target the in-vivo parcellation of the cortex at 800µm resolution or higher, using the multi-parameter maps as markers of myeloarchitecture.


Primary contact

Nikolaus Weiskopf (n.weiskopf «at» ucl.ac.uk)



[1]        A. Lutti, C. Hutton, J. Finsterbusch, G. Helms, and N. Weiskopf, “Optimization and validation of methods for mapping of the radiofrequency transmit field at 3T,” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 64, no. 1, pp. 229-238, Jul. 2010.http://dx.doi.org/10.1002/mrm.22421

[2]        N. Weiskopf, A. Lutti, G. Helms, M. Novak, J. Ashburner, and C. Hutton, “Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT),” NeuroImage, vol. 54, no. 3, pp. 2116-2124, Feb. 2011. http://dx.doi.org/10.1016/j.neuroimage.2010.10.023

[3]        G. Helms, B. Draganski, R. Frackowiak, J. Ashburner, and N. Weiskopf, “Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps,” Neuroimage, vol. 47, no. 1, pp. 194-198, 2009. http://dx.doi.org/10.1016/j.neuroimage.2009.03.053

[4]        B. Draganski et al., “Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ),” NeuroImage, vol. 55, no. 4, pp. 1423-1434, Apr. 2011. http://dx.doi.org/10.1016/j.neuroimage.2011.01.052

[5]        L. Garrido et al., “Voxel-based morphometry reveals reduced grey matter volume in the temporal cortex of developmental prosopagnosics,” Brain: A Journal of Neurology, vol. 132, no. 12, pp. 3443-3455, Dec. 2009. http://dx.doi.org/10.1093/brain/awp271

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