  Adaptive intelligence aims at empowering machine learning techniques with the
extensive use of domain knowledge. In this work, we present the application of
adaptive intelligence to accelerate MR acquisition. Starting from undersampled
k-space data, an iterative learning-based reconstruction scheme inspired by
compressed sensing theory is used to reconstruct the images. We adopt deep
neural networks to refine and correct prior reconstruction assumptions given
the training data. Our results show that an adaptive intelligence approach
performs better than traditional methods as well as deep learning methods that
do not take prior knowledge into account.
