Physically-primed deep-neural-networks for generalized undersampled MRI reconstruction
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A plethora of deep-neural-networks (DNN) based methods were proposed over the past few years to address the challenging ill-posed inverse problem of MRI reconstruction from undersampled "k-space" (Fourier domain) data. However, instability against variations in the acquisition process and the anatomical distribution, indicates a poor generalization of the relevant physical models by the DNN architectures compared to their classical counterparts. The poor generalization effectively precludes DNN applicability for undersampled MRI reconstruction in the clinical setting. We improve the generalization capacity of DNN methods for undersampled MRI reconstruction by introducing a physically-primed DNN architecture and training approach. Our architecture encodes the undersampling mask in addition to the observed data in the model architecture and employs an appropriate training approach that uses data generated with various undersampling masks to encourage the model to generalize the undersampled MRI reconstruction problem. We demonstrated the added-value of our approach through extensive experimentation with the publicly available Fast-MRI dataset. Our physically-primed approach achieved an enhanced generalization capacity which resulted in significantly improved robustness against variations in the acquisition process and in the anatomical distribution, especially in pathological regions, compared to both vanilla DNN methods and DNN trained with undersampling mask augmentation. Trained models and code to replicate our experiments will become available for research purposes upon acceptance.
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