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Abstract #4776

Fidelity Imposing Network Edit (FINE) for Solving Ill-Posed Image Reconstruction

Jinwei Zhang1,2, Zhe Liu1,2, Shun Zhang2, Pascal Spincemaille2, Thanh D. Nguyen2, Mert R. Sabuncu1,3, and Yi Wang1,2

1Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States, 2Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States, 3Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States

A Fidelity Imposing Network Edit (FINE) method is proposed for solving inverse problem that edits a pre-trained network's weights with the physical forward model for the test data to overcome the breakdown of deep learning (DL) based image reconstructions when the test data significantly deviates from the training data. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and undersampled multi-contrast reconstruction in MRI.

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