SVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction

Model-based deep learning reconstruction with a nonlinear encoding matrix poses unique challenges to GPU memory, due to the densely connected computational graph nodes in the physics model part. In this work, SVD compression is demonstrated as necessary for such networks, and it is applied to the highly nonlinear case of Bloch-Siegert encoding from a low-field MR scanner. The redundancy across all nonlinear encoding dimensions is exploited for compression. With the compressed encoding matrix, the model-based network is feasible to implement. It outperforms the traditional reconstruction at all levels of simulated Gaussian noise and has advantages over commonly used regularization terms.