k-space deep learning (DL) is emerging as an alternative to the conventional image domain DL for accelerated MRI. Typically, DL requires training on large amounts of data, which is unaccessible in clinical. This paper proposes to present an untrained k-space deep generative model (DGM) to interpolate missing data. Specifically, missing data is interpolated by a carefully designed untrained generator, of which the output layer conforms the MR image multichannel prior, while the architecture of other layers implicitly captures k-space statistics priors. Furthermore, we prove that the proposed method guarantees enough accuracy bounds for interpolated data under commonly used sampling patterns.
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