Deep Fourier-Space Inversion

Guiding the network architecture to learn to apply the kernel inversely in Fourier space allows training to be less prone to overfitting. Using simulated images from a single brain image, it is possible to satisfactorily reconstruct a susceptibility map of the abdomen. By having as input the Fourier space of the local field and the kernel of the dipole, the network learned to reduce the noise, to divide the data of the local field by the kernel where possible and to recover the data in the magic cone.