Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse
  Training Data
Kang Lin Reinhard Heckel
Deep learning based methods for image reconstruction are state-of-the-art for
a variety of imaging tasks. However, neural networks often perform worse if the
training data differs significantly from the data they are applied to. For
example, a network trained for accelerated magnetic resonance imaging (MRI) on
one scanner performs worse on another scanner. In this work, we investigate the
impact of the training data on the model's performance and robustness for
accelerated MRI. We find that models trained on the combination of various data
distributions, such as those obtained from different MRI scanners and
anatomies, exhibit robustness equal or superior to models trained on the best
single distribution for a specific target distribution. Thus training on
diverse data tends to improve robustness. Furthermore, training on diverse data
does not compromise in-distribution performance, i.e., a model trained on
diverse data yields in-distribution performance at least as good as models
trained on the more narrow individual distributions. Our results suggest that
training a model for imaging on a variety of distributions tends to yield a
more effective and robust model than maintaining separate models for individual
distributions.