Inhomogeneity of the radiofrequency field (B1) is one of the main problems in quantitative MRI. Leveraging from the unique ability of deep learning, we propose a data driven strategy to derive quantitative B1 map from a single qualitative MR image without specific requirements on the weighting of the input image. B1 estimation is accomplished using a self-attention deep convolutional neural network, which makes efficient use of local and non-local information. Without additional data acquisition, an accurate estimation of B1 map is achieved, which is useful for the compensation of field inhomogeneity in T1 mapping as well as for other applications.
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