  Recently, deep neural networks have greatly advanced undersampled Magnetic
Resonance Image (MRI) reconstruction, wherein most studies follow the
one-anatomy-one-network fashion, i.e., each expert network is trained and
evaluated for a specific anatomy. Apart from inefficiency in training multiple
independent models, such convention ignores the shared de-aliasing knowledge
across various anatomies which can benefit each other. To explore the shared
knowledge, one naive way is to combine all the data from various anatomies to
train an all-round network. Unfortunately, despite the existence of the shared
de-aliasing knowledge, we reveal that the exclusive knowledge across different
anatomies can deteriorate specific reconstruction targets, yielding overall
performance degradation. Observing this, in this study, we present a novel deep
MRI reconstruction framework with both anatomy-shared and anatomy-specific
parameterized learners, aiming to "seek common ground while reserving
differences" across different anatomies.Particularly, the primary
anatomy-shared learners are exposed to different anatomies to model flourishing
shared knowledge, while the efficient anatomy-specific learners are trained
with their target anatomy for exclusive knowledge. Four different
implementations of anatomy-specific learners are presented and explored on the
top of our framework in two MRI reconstruction networks. Comprehensive
experiments on brain, knee and cardiac MRI datasets demonstrate that three of
these learners are able to enhance reconstruction performance via multiple
anatomy collaborative learning.
