  Objective: To allow efficient learning using the Recurrent Inference Machine
(RIM) for image reconstruction whereas not being strictly dependent on the
training data distribution so that unseen modalities and pathologies are still
accurately recovered. Methods: Theoretically, the RIM learns to solve the
inverse problem of accelerated-MRI reconstruction whereas being robust to
variable imaging conditions. The efficiency and generalization capabilities
with different training datasets were studied, as well as recurrent network
units with decreasing complexity: the Gated Recurrent Unit (GRU), the Minimal
Gated Unit (MGU), and the Independently Recurrent Neural Network (IndRNN), to
reduce inference times. Validation was performed against Compressed Sensing
(CS) and further assessed based on data unseen during training. A pathology
study was conducted by reconstructing simulated white matter lesions and
prospectively undersampled data of a Multiple Sclerosis patient. Results:
Training on a single modality of 3T $T_1$-weighted brain data appeared
sufficient to also reconstruct 7T $T_{2}^*$-weighted brain and 3T
$T_2$-weighted knee data. The IndRNN is an efficient recurrent unit, reducing
inference time by 68\% compared to CS, whereas maintaining performance. The RIM
was able to reconstruct lesions unseen during training more accurately than CS
when trained on $T_2$-weighted knee data. Training on $T_1$-weighted brain data
and on combined data slightly enhanced the signal compared to CS. Conclusion:
The RIM is efficient when decreasing its complexity, which reduces the
inference time, whereas still being able to reconstruct data and pathology that
was unseen during training.
