In this work, we propose a spatially adaptive cross-modality based three-dimensional reconstruction network to determine the susceptibility distribution from the magnetic field measurement. To compensate the information lost in previous encoder layers, a set of spatially adaptive modules in different resolutions are embedded into multiscale decoders, which extract features from magnitude images and field maps adaptively. Thus, the magnitude regularization is incorporated into the network architecture while the training stability is improved. It is potential to solve inverse problems of three-dimensional data, especially for cross-modality related reconstructions.
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