  In this study, we develop a physics-informed deep learning-based method to
synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a
single five-minute acquisition and investigate its ability to generalize to
arbitrary contrasts to accelerate neuroimaging protocols. A dataset of
fifty-five subjects acquired with a standard MRI protocol and a five-minute
transient-state sequence was used to develop a physics-informed deep
learning-based method. The model, based on a generative adversarial network,
maps data acquired from the five-minute scan to "effective" quantitative
parameter maps, here named q*-maps, by using its generated PD, T1, and T2
values in a signal model to synthesize four standard contrasts (proton
density-weighted, T1-weighted, T2-weighted, and T2-weighted fluid-attenuated
inversion recovery), from which losses are computed. The q*-maps are compared
to literature values and the synthetic contrasts are compared to an end-to-end
deep learning-based method proposed by literature. The generalizability of the
proposed method is investigated for five volunteers by synthesizing three
non-standard contrasts unseen during training and comparing these to respective
ground truth acquisitions via contrast-to-noise ratio and quantitative
assessment. The physics-informed method was able to match the high-quality
synthMRI of the end-to-end method for the four standard contrasts, with mean
\pm standard deviation structural similarity metrics above 0.75 \pm 0.08 and
peak signal-to-noise ratios above 22.4 \pm 1.9 and 22.6 \pm 2.1. Additionally,
the physics-informed method provided retrospective contrast adjustment, with
visually similar signal contrast and comparable contrast-to-noise ratios to the
ground truth acquisitions for three sequences unused for model training,
demonstrating its generalizability and potential application to accelerate
neuroimaging protocols.
