  Magnetic Resonance (MR) images suffer from various types of artifacts due to
motion, spatial resolution, and under-sampling. Conventional deep learning
methods deal with removing a specific type of artifact, leading to separately
trained models for each artifact type that lack the shared knowledge
generalizable across artifacts. Moreover, training a model for each type and
amount of artifact is a tedious process that consumes more training time and
storage of models. On the other hand, the shared knowledge learned by jointly
training the model on multiple artifacts might be inadequate to generalize
under deviations in the types and amounts of artifacts. Model-agnostic
meta-learning (MAML), a nested bi-level optimization framework is a promising
technique to learn common knowledge across artifacts in the outer level of
optimization, and artifact-specific restoration in the inner level. We propose
curriculum-MAML (CMAML), a learning process that integrates MAML with
curriculum learning to impart the knowledge of variable artifact complexity to
adaptively learn restoration of multiple artifacts during training. Comparative
studies against Stochastic Gradient Descent and MAML, using two cardiac
datasets reveal that CMAML exhibits (i) better generalization with improved
PSNR for 83% of unseen types and amounts of artifacts and improved SSIM in all
cases, and (ii) better artifact suppression in 4 out of 5 cases of composite
artifacts (scans with multiple artifacts).
