  We introduce a fast model based deep learning approach for calibrationless
parallel MRI reconstruction. The proposed scheme is a non-linear generalization
of structured low rank (SLR) methods that self learn linear annihilation
filters from the same subject. It pre-learns non-linear annihilation relations
in the Fourier domain from exemplar data. The pre-learning strategy
significantly reduces the computational complexity, making the proposed scheme
three orders of magnitude faster than SLR schemes. The proposed framework also
allows the use of a complementary spatial domain prior; the hybrid
regularization scheme offers improved performance over calibrated image domain
MoDL approach. The calibrationless strategy minimizes potential mismatches
between calibration data and the main scan, while eliminating the need for a
fully sampled calibration region.
