  Accelerated MRI reconstructs images of clinical anatomies from sparsely
sampled signal data to reduce patient scan times. While recent works have
leveraged deep learning to accomplish this task, such approaches have often
only been explored in simulated environments where there is no signal
corruption or resource limitations. In this work, we explore augmentations to
neural network MRI image reconstructors to enhance their clinical relevancy.
Namely, we propose a ConvNet model for detecting sources of image artifacts
that achieves a classifier $F_2$ score of 79.1%. We also demonstrate that
training reconstructors on MR signal data with variable acceleration factors
can improve their average performance during a clinical patient scan by up to
2%. We offer a loss function to overcome catastrophic forgetting when models
learn to reconstruct MR images of multiple anatomies and orientations. Finally,
we propose a method for using simulated phantom data to pre-train
reconstructors in situations with limited clinically acquired datasets and
compute capabilities. Our results provide a potential path forward for clinical
adaptation of accelerated MRI.
