  Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers
from prolonged scan times. Reconstruction methods can alleviate this limitation
by recovering clinically usable images from accelerated acquisitions. In
particular, learning-based methods promise performance leaps by employing deep
neural networks as data-driven priors. A powerful approach uses scan-specific
(SS) priors that leverage information regarding the underlying physical signal
model for reconstruction. SS priors are learned on each individual test scan
without the need for a training dataset, albeit they suffer from
computationally burdening inference with nonlinear networks. An alternative
approach uses scan-general (SG) priors that instead leverage information
regarding the latent features of MRI images for reconstruction. SG priors are
frozen at test time for efficiency, albeit they require learning from a large
training dataset. Here, we introduce a novel parallel-stream fusion model
(PSFNet) that synergistically fuses SS and SG priors for performant MRI
reconstruction in low-data regimes, while maintaining competitive inference
times to SG methods. PSFNet implements its SG prior based on a nonlinear
network, yet it forms its SS prior based on a linear network to maintain
efficiency. A pervasive framework for combining multiple priors in MRI
reconstruction is algorithmic unrolling that uses serially alternated
projections, causing error propagation under low-data regimes. To alleviate
error propagation, PSFNet combines its SS and SG priors via a novel
parallel-stream architecture with learnable fusion parameters. Demonstrations
are performed on multi-coil brain MRI for varying amounts of training data.
PSFNet outperforms SG methods in low-data regimes, and surpasses SS methods
with few tens of training samples.
