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Abstract

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple
properties of biological tissues. It relies on a pseudo-random acquisition and the
matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary
is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping
of only a small number of parameters (proton density, T1 and T2 in general). Inspired by
diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence
with embedded diffusion-encoding gradients along all three axes to eciently encode orientational
diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural
network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled
acquisition. We bypass expensive dictionary matching by learning the implicit
physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion
tensor parameters. The predicted parameter maps and the derived scalar diffusion
metrics agree well with state-of-the-art reference protocols. Orientational diffusion information
is captured as seen from the estimated primary diffusion directions. In addition to
this, the joint acquisition and reconstruction framework proves capable of preserving tissue
abnormalities in multiple sclerosis lesions.