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Typical quantitative MRI (qMRI) methods estimate parameter maps after imagereconstructing, which is prone to biases and error propagation. We propose aNonlinear Conjugate Gradient (NLCG) optimizer for model-based T2/T1 estimation,which incorporates U-Net regularization trained in a scan-specific manner. Thisend-to-end method directly estimates qMRI maps from undersampled k-space datausing mono-exponential signal modeling with zero-shot scan-specific neuralnetwork regularization to enable high fidelity T1 and T2 mapping. T2 and T1mapping results demonstrate the ability of the proposed NLCG-Net to improveestimation quality compared to subspace reconstruction at high accelerations.

NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction