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Abstract #3980

Exploring domain adaption for deep neural network trained QSM

JUAN LIU1 and Kevin Koch2
1Yale University, New Haven, CT, United States, 2Medical College of Wisconsin, Milwaukee, WI, United States

We aim to address the domain adaption problem of neural networks for QSM reconstruction which are learned from synthetic data while applied on real data. To address the unsupervised domain adaption, we apply domain-specific batch normalization layers in convolutional neural networks while allowing them to share all other model parameters. The proposed method is evaluated on multiple orientation datasets and single-orientation QSM datasets. Compared withTKD, MEDI, and DL-based method first training on synthetic datasets then model-based fine-tuning on real datasets, the proposed method achieved better performance.

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