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.

翻译：典型的定量磁共振成像（qMRI）方法通常在图像重建后估算参数图，这容易产生偏差和误差传播。我们提出了一种用于基于模型的T2/T1估计的非线性共轭梯度（NLCG）优化器，该优化器结合了以扫描特定方式训练的U-Net正则化。这种端到端方法利用单指数信号建模，配合零样本扫描特定神经网络正则化，直接从欠采样的k空间数据中估算qMRI图，从而实现高保真度的T1和T2映射。T2和T1映射结果表明，与高加速条件下的子空间重建相比，所提出的NLCG-Net能够提升估计质量。