Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation.
Goal(s): To propose an end-to-end method to directly estimate qMRI maps from undersampled k-space data using model-based reconstruction and zero-shot network regularization.
Approach: 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.
Results: T2 and T1 mapping results demonstrate the ability of the proposed NLCG-Net to improve estimation quality compared to subspace reconstruction at high accelerations.
Impact: We propose a model-based qMRI technique, NLCG-Net, that incorporates mono-exponential signal modeling with zero-shot scan-specific neural network regularization to enable high fidelity T1 and T2 mapping.
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