Physics-based network fine-tuning for robust susceptibility mapping from high-pass filtered phase
Jinwei Zhang1, Alexey Dimov2, Hang Zhang1, Chao Li1, Thanh Nguyen2, Pascal Spincemaille2, and Yi Wang2
1Cornell University, New York, NY, United States, 2Weill Cornell Medicine, New York, NY, United States
A network fine-tuning step based on signal physics is proposed for deep learning based quantitative susceptibility mapping using high-pass filtered phase only to susceptibility. The proposed method showed better robustness compared to the pre-trained networks without fine-tuning when the test dataset deviated from the training dataset, such as a change in voxel size or high-pass filter cutoff frequency.
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