  Deep learning image reconstruction algorithms often suffer from model
mismatches when the acquisition scheme differs significantly from the forward
model used during training. We introduce a Generalized Stein's Unbiased Risk
Estimate (GSURE) loss metric to adapt the network to the measured k-space data
and minimize model misfit impact. Unlike current methods that rely on the mean
square error in kspace, the proposed metric accounts for noise in the
measurements. This makes the approach less vulnerable to overfitting, thus
offering improved reconstruction quality compared to schemes that rely on
mean-square error. This approach may be useful to rapidly adapt pre-trained
models to new acquisition settings (e.g., multi-site) and different contrasts
than training data
