Fig. 4.

Example of the input training data for three DL reconstruction methods. The fully-supervised MoDL method [25] receives var-dens sampled data as input and uses the entire k-space for supervision. The self-supervised SSDU method [132] receives var-dens data as input, splits it into two subsets, and uses one set for data consistency and the other for supervision. In this example, the var-dens data were sampled from parallel-imaging (equispaced) acquired data, as in [132]. The k-band method [135] receives var-dens sampled data from a k-space band, and uses data from the whole band for supervision, without any supervision outside the band. Different bands are acquired from different subjects, with random orientations. At inference, the input to all three methods is var-dens data from the entire k-space, similar to that shown here for MoDL