Integrating Adaptive Generative Network and Subspace Models for Accelerated MR Parameter Mapping

We proposed a new method that integrates an adapted generative network image prior and subspace modeling for accelerated MR parameter mapping. Specifically, a formulation is introduced to synergize a subspace constraint, a subject-specific generative model-based image representation and joint sparsity regularization. A pretraining using public database plus subjective specific network adaptation strategy is used to construct an accurate representation of the unknown contrast-weighted images. An efficient alternating minimization algorithm is used to solve the resulting optimization problem. The improved reconstruction performance achieved by the proposed method over subspace and sparsity constraints was demonstrated in a T2 mapping experiment.