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In 2D multi-slice magnetic resonance (MR) acquisition, the through-planesignals are typically of lower resolution than the in-plane signals. Whilecontemporary super-resolution (SR) methods aim to recover the underlyinghigh-resolution volume, the estimated high-frequency information is implicitvia end-to-end data-driven training rather than being explicitly stated andsought. To address this, we reframe the SR problem statement in terms ofperfect reconstruction filter banks, enabling us to identify and directlyestimate the missing information. In this work, we propose a two-stage approachto approximate the completion of a perfect reconstruction filter bankcorresponding to the anisotropic acquisition of a particular scan. In stage 1,we estimate the missing filters using gradient descent and in stage 2, we usedeep networks to learn the mapping from coarse coefficients to detailcoefficients. In addition, the proposed formulation does not rely on externaltraining data, circumventing the need for domain shift correction. Under ourapproach, SR performance is improved particularly in "slice gap" scenarios,likely due to the constrained solution space imposed by the framework.

Deep filter bank regression for super-resolution of anisotropic MR brain images