A Generative Subspace Model for High-dimensional MR Imaging

Subspace or low-rank models have been demonstrated useful in producing efficient representations and reconstructions for high dimensional imaging problems. In this study, we propose an unsupervised learning-based approach by generalizing the subspace model using a deep generative network. The generative subspace model can then be incorporated into the physics-based reconstruction formalism. The network parameters can be self-trained by minimizing the cost function with the flexibility to integrate with conventional constraints. We demonstrated the effectiveness of the proposed method over standard linear subspace and deep image prior models using in vivo T2 mapping dataset.