Supervised deep learning (DL) methods for MRI reconstruction is promising due to their improved reconstruction quality compared with traditional approaches. However, all current DL methods do not utilise anatomical features, a potentially useful prior, for regularising the network. This preliminary work presents a 3D CNN-based training framework that attempts to incorporate learning of anatomy prior to enhance model’s generalisation and its stability to perturbation. Preliminary results on single-channel HCP, unseen pathological HCP and IXI volumetric data (effective R=16) suggest its potential capability for achieving high acceleration while being robust against unseen anomalous data and data acquired from different MRI systems.
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