Diffusion MRI provides noninvasively information of tissue microstructure. Current models allow to empirically analyze data or to provide more insightful information on the tissue features. However, those models require strong assumptions on the underlying tissues and the acquisition of large image data sets with different acquisition parameters. We have investigated a new, model free approach which enables classification of tissue types from the “proximity” or resemblance of their diffusion MRI signal profile at a sparse set of key b values (maximizing sensitivity to tissue microstructure) to a library of “signature” signal profiles (e.g. typical brain grey and white matter).
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