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.

本研究开发了一种基于物理知识的深度学习方法来合成多种脑部磁共振成像（MRI）对比度，从而加速神经影像处理，并研究了该方法的泛化能力，该方法能够将五分钟的扫描数据合成为四种标准对比度，并且具有良好的泛化性能适用于加速神经影像处理的协议。