Accurate reconstruction using parallel imaging relies on estimating a set of sensitivity maps from a fully-sampled calibration region, which can lead to reconstruction artifacts in poor signal-to-noise ratio conditions. We introduce Deep J-Sense as a deep learning approach for jointly estimating the image and the sensitivity maps in the frequency-domain. We formulate an alternating minimization problem that uses convolutional neural networks for regularization and train the unrolled model end-to-end. We compare reconstruction performance with model-based deep learning methods that only optimize the image and show that our approach is superior.
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