  Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing
technique that quantifies the magnetic susceptibility of body tissue from phase
data. However, the traditional QSM reconstruction pipeline involves multiple
non-trivial steps, including phase unwrapping, background field removal, and
dipole inversion. These intermediate steps not only increase the reconstruction
time but amplify noise and errors. This study develops a large-stencil
Laplacian preprocessed deep learning-based neural network for near instant
quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MR
phase data. The proposed iQFM and iQSM methods were compared with established
reconstruction pipelines on simulated and in vivo datasets. In addition,
experiments on patients with intracranial hemorrhage and multiple sclerosis
were also performed to test the generalization of the novel neural networks.
The proposed iQFM and iQSM methods yielded comparable results to multi-step
methods in healthy subjects while dramatically improving reconstruction
accuracies on intracranial hemorrhages with large susceptibilities. The
reconstruction time was also substantially shortened from minutes using
multi-step methods to only 30 milliseconds using the trained iQFM and iQSM
neural networks.
