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Abstract #4356

Accelerate MR imaging by anatomy-driven deep learning image reconstruction

Vick Lau1,2, Christopher Man1,2, Yilong Liu1,2, Alex T. L. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China


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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