  Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI
technique that can extract important tissue and system parameters such as T1,
T2, B0, and B1 from a single scan. This property also makes it attractive for
retrospectively synthesizing contrast-weighted images. In general,
contrast-weighted images like T1-weighted, T2-weighted, etc., can be
synthesized directly from parameter maps through spin-dynamics simulation
(i.e., Bloch or Extended Phase Graph models). However, these approaches often
exhibit artifacts due to imperfections in the mapping, the sequence modeling,
and the data acquisition. Here we propose a supervised learning-based method
that directly synthesizes contrast-weighted images from the MRF data without
going through the quantitative mapping and spin-dynamics simulation. To
implement our direct contrast synthesis (DCS) method, we deploy a conditional
Generative Adversarial Network (GAN) framework and propose a multi-branch U-Net
as the generator. The input MRF data are used to directly synthesize
T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR)
images through supervised training on paired MRF and target spin echo-based
contrast-weighted scans. In-vivo experiments demonstrate excellent image
quality compared to simulation-based contrast synthesis and previous DCS
methods, both visually as well as by quantitative metrics. We also demonstrate
cases where our trained model is able to mitigate in-flow and spiral
off-resonance artifacts that are typically seen in MRF reconstructions and thus
more faithfully represent conventional spin echo-based contrast-weighted
images.
