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2019

Generalizing Deep Learning MRI Reconstruction across Different Domains

Ouyang, Cheng, Schlemper, Jo, Biffi, Carlo et al.

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We look into the robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.

We then propose to generalize the network by training with large publicly-available natural image datasets with synthesized phase information to achieve high cross-domain reconstruction performance which is competitive with domain-specific training.
To explain its generalization mechanism, we have also analyzed patch sets for different training datasets.

Generalizing Deep Learning MRI Reconstruction across Different Domains · Around

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“Microsoft coco: Common objects in context,”
Lin et al., · 2014
Earlier work this paper cites.
“A transfer-learning approach for accelerated mri using deep neural networks,”
Original
Dar et al., · 2017
Earlier work this paper cites.

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“A deep cascade of convolutional neural networks for dynamic mr image reconstruction,”
Schlemper et al., · 2018
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“Learning a variational network for reconstruction of accelerated mri data,”
Hammernik et al., · 2018
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