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Journal Article

Synthetic quantitative MRI through relaxometry modelling

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Weiskopf,  Nikolaus
Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Callaghan_Mohammadi_2016.pdf
(Publisher version), 748KB

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Citation

Callaghan, M., Mohammadi, S., & Weiskopf, N. (2016). Synthetic quantitative MRI through relaxometry modelling. NMR in Biomedicine, 29(12), 1729-1738. doi:10.1002/nbm.3658.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-A661-7
Abstract
Quantitative MRI (qMRI) provides standardized measures of specific physical parameters that are sensitive to the underlying tissue microstructure and are a first step towards achieving maps of biologically relevant metrics through in vivo histology using MRI. Recently proposed models have described the interdependence of qMRI parameters. Combining such models with the concept of image synthesis points towards a novel approach to synthetic qMRI, in which maps of fundamentally different physical properties are constructed through the use of biophysical models. In this study, the utility of synthetic qMRI is investigated within the context of a recently proposed linear relaxometry model. Two neuroimaging applications are considered. In the first, artefact-free quantitative maps are synthesized from motion-corrupted data by exploiting the over-determined nature of the relaxometry model and the fact that the artefact is inconsistent across the data. In the second application, a map of magnetization transfer (MT) saturation is synthesized without the need to acquire an MT-weighted volume, which directly leads to a reduction in the specific absorption rate of the acquisition. This feature would be particularly important for ultra-high field applications. The synthetic MT map is shown to provide improved segmentation of deep grey matter structures, relative to segmentation using T1 -weighted images or R1 maps. The proposed approach of synthetic qMRI shows promise for maximizing the extraction of high quality information related to tissue microstructure from qMRI protocols and furthering our understanding of the interrelation of these qMRI parameters.