  Convolutional Neural network-based MR reconstruction methods have shown to
provide fast and high quality reconstructions. A primary drawback with a
CNN-based model is that it lacks flexibility and can effectively operate only
for a specific acquisition context limiting practical applicability. By
acquisition context, we mean a specific combination of three input settings
considered namely, the anatomy under study, undersampling mask pattern and
acceleration factor for undersampling. The model could be trained jointly on
images combining multiple contexts. However the model does not meet the
performance of context specific models nor extensible to contexts unseen at
train time. This necessitates a modification to the existing architecture in
generating context specific weights so as to incorporate flexibility to
multiple contexts. We propose a multiple acquisition context based network,
called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition
contexts and generalizable to unseen contexts for applicability in real
scenarios. The proposed network has an MRI reconstruction module and a dynamic
weight prediction (DWP) module. The DWP module takes the corresponding
acquisition context information as input and learns the context-specific
weights of the reconstruction module which changes dynamically with context at
run time. We show that the proposed approach can handle multiple contexts based
on cardiac and brain datasets, Gaussian and Cartesian undersampling patterns
and five acceleration factors. The proposed network outperforms the naive
jointly trained model and gives competitive results with the context-specific
models both quantitatively and qualitatively. We also demonstrate the
generalizability of our model by testing on contexts unseen at train time.
