  Typical quantitative MRI (qMRI) methods estimate parameter maps after image
reconstructing, which is prone to biases and error propagation. We propose a
Nonlinear Conjugate Gradient (NLCG) optimizer for model-based T2/T1 estimation,
which incorporates U-Net regularization trained in a scan-specific manner. This
end-to-end method directly estimates qMRI maps from undersampled k-space data
using mono-exponential signal modeling with zero-shot scan-specific neural
network regularization to enable high fidelity T1 and T2 mapping. T2 and T1
mapping results demonstrate the ability of the proposed NLCG-Net to improve
estimation quality compared to subspace reconstruction at high accelerations.
