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Abstract #3977

Generalization of deep learning-based QSM by expanding the diversity of spatial gradient in training data

Woojin Jung1, Steffen Bollmann2, Se-Hong Oh3, Hyeong-geol Shin1, Sooyeon Ji1, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2The University of Queensland, Brisbane, Australia, 3Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of

In this work, the effect of spatial gradients in the training data on deep learning-based QSM is explored. We observe that deep learning-based QSM underestimates the susceptibility values when spatial gradients differ between training and test data. For demonstration, three types of networks were trained by using different spatial gradients of training images and evaluated on test data with varying spatial gradients. The results indicate that expanding the spatial gradient distribution of training data improves the performance of deep learning-based QSM. Furthermore, we demonstrate that augmenting spatial gradients may improve deep-learning based QSM to work for various image resolutions.

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