Simulation study demonstrates that deep learning with spherical harmonic constraints improves B0-field map estimation in low-field MRI, indicating a path toward distortion-free imaging.
Heavy B0-field inhomogeneities in low-field MRI can lead to geometric distortions in the reconstruction, if not compensated. We simulated low-field data to evaluate different approaches for estimating the field map from phase difference maps. By using spherical harmonic basis functions as physical constraints in our neural network approach we could improve the estimation of B0-field maps compared to other network architectures and methods not involving neural networks.
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Schote et al. (2024) studied this question.
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