NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled
  Quantitative MRI Reconstruction
Xinrui Jiang Yohan Jun Jaejin Cho Mengze Gao Xingwang Yong Berkin Bilgic
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.