This work proposes a deep-learning based computationally efficient method to reduce phase errors in multi-shot diffusion-weighted images. Recently, we introduced a navigator-free method to reduce these errors using structured low-rank matrix completion based approach termed as MUSSELS. This approach produces state-of-the-art results but is computationally expensive and requires a matrix lifting to estimate filter bank for each direction and slice, independently. We propose a generic deep-learning based approach to estimate phase errors for an unseen dataset by pre-learning the denoiser from exemplary data. The resulting model-based deep learning architecture reduces the computation time by a factor of around 450 with comparable reconstruction quality.
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