  Deep learning holds great promise in the reconstruction of undersampled
Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate
the performance of rapid MRI. In existing deep learning-based reconstruction
methods, supervised training is performed using artifact-free reference images
and their corresponding undersampled pairs. The undersampled images are
generated by a fixed undersampling pattern in the training, and the trained
network is then applied to reconstruct new images acquired with the same
pattern in the inference. While such a training strategy can maintain a
favorable reconstruction for a pre-selected undersampling pattern, the
robustness of the trained network against any discrepancy of undersampling
schemes is typically poor. We developed a novel deep learning-based
reconstruction framework called SANTIS for efficient MR image reconstruction
with improved robustness against sampling pattern discrepancy. SANTIS uses a
data cycle-consistent adversarial network combining efficient end-to-end
convolutional neural network mapping, data fidelity enforcement and adversarial
training for reconstructing accelerated MR images more faithfully. A training
strategy employing sampling augmentation with extensive variation of
undersampling patterns was further introduced to promote the robustness of the
trained network. Compared to conventional reconstruction and standard deep
learning methods, SANTIS achieved consistent better reconstruction performance,
with lower errors, greater image sharpness and higher similarity with respect
to the reference regardless of the undersampling patterns during inference.
This novel concept behind SANTIS can particularly be useful towards improving
the robustness of deep learning-based image reconstruction against discrepancy
between training and evaluation, which is currently an important but less
studied open question.
