Physics-based network fine-tuning for robust quantitative susceptibility
  mapping from high-pass filtered phase
Jinwei Zhang Alexey Dimov Chao Li Hang Zhang Thanh D. Nguyen Pascal Spincemaille Yi Wang
Purpose: To improve the generalization ability of convolutional neural
network (CNN) based prediction of quantitative susceptibility mapping (QSM)
from high-pass filtered phase (HPFP) image. Methods: The proposed network
addresses two common generalization issues that arise when using a pre-trained
network to predict QSM from HPFP: a) data with unseen voxel sizes, and b) data
with unknown high-pass filter parameters. A network fine-tuning step based on a
high-pass filtering dipole convolution forward model is proposed to reduce the
generalization error of the pre-trained network. A progressive Unet
architecture is proposed to improve prediction accuracy without increasing
fine-tuning computational cost. Results: In retrospective studies using RMSE,
PSNR, SSIM and HFEN as quality metrics, the performance of both Unet and
progressive Unet was improved after physics-based fine-tuning at all voxel
sizes and most high-pass filtering cutoff frequencies tested in the experiment.
Progressive Unet slightly outperformed Unet both before and after fine-tuning.
In a prospective study, image sharpness was improved after physics-based
fine-tuning for both Unet and progressive Unet. Compared to Unet, progressive
Unet had better agreement of regional susceptibility values with reference QSM.
Conclusion: The proposed method shows improved robustness compared to the
pre-trained network without fine-tuning when the test dataset deviates from
training. Our code is available at https://github.com/Jinwei1209/SWI_to_QSM/