Multi-echo MRI Reconstruction with Iteratively Refined Zero-shot Spatio-Temporal Deep Generative Prior

The subspace method has been widely used for multi-echo/contrast MRI reconstruction, assuming the temporal MR signal evolution can be compactly represented using a few linear coefficients. Recently, methods based on artificial neural networks (trained with large datasets) enabled nonlinear representations of temporal or spatio-temporal MR signals and demonstrated improved performance. This work proposes a novel zero-shot spatio-temporal generative prior for multi-echo/contrast MRI reconstruction, assuming the spatio-temporal MR signals can be nonlinearly generated using deep generative neural networks without external training data. The proposed method was evaluated with 3D-QALAS and EPTI data, and exhibited substantial improvement in NRMSE against existing methods.