  Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric
quantitative MR imaging technique, for which image reconstruction methods
utilizing low-rank and subspace constraints have achieved state-of-the-art
performance. However, this class of methods often suffers from an
ill-conditioned model-fitting issue, which degrades the performance as the data
acquisition lengths become short and/or the signal-to-noise ratio becomes low.
To address this problem, we present a new image reconstruction method for MR
Fingerprinting, integrating low-rank and subspace modeling with a deep
generative prior. Specifically, the proposed method captures the strong
spatiotemporal correlation of contrast-weighted time-series images in MR
Fingerprinting via a low-rank factorization. Further, it utilizes an untrained
convolutional generative neural network to represent the spatial subspace of
the low-rank model, while estimating the temporal subspace of the model from
simulated magnetization evolutions generated based on spin physics. Here the
architecture of the generative neural network serves as an effective
regularizer for the ill-conditioned inverse problem without additional spatial
training data that are often expensive to acquire. The proposed formulation
results in a non-convex optimization problem, for which we develop an algorithm
based on variable splitting and alternating direction method of multipliers.We
evaluate the performance of the proposed method with numerical simulations and
in vivo experiments and demonstrate that the proposed method outperforms the
state-of-the-art low-rank and subspace reconstruction.
