Calibrationless Parallel MRI Using Model Based Deep Learning (C-MODL)

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; the proposed scheme pre-learns the nonlinear 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 main scan, while eliminating the need for a fully sampled calibration region.