  Fast spin-echo (FSE) pulse sequences for Magnetic Resonance Imaging (MRI)
offer important imaging contrast in clinically feasible scan times.
T2-shuffling is widely used to resolve temporal signal dynamics in FSE
acquisitions by exploiting temporal correlations via linear latent space and a
predefined regularizer. However, predefined regularizers fail to exploit the
incoherence especially for 2D acquisitions.Recent self-supervised learning
methods achieve high-fidelity reconstructions by learning a regularizer from
undersampled data without a standard supervised training data set. In this
work, we propose a novel approach that utilizes a self supervised learning
framework to learn a regularizer constrained on a linear latent space which
improves time-resolved FSE images reconstruction quality. Additionally, in
regimes without groundtruth sensitivity maps, we propose joint estimation of
coil-sensitivity maps using an iterative reconstruction technique. Our
technique functions is in a zero-shot fashion, as it only utilizes data from a
single scan of highly undersampled time series images. We perform experiments
on simulated and retrospective in-vivo data to evaluate the performance of the
proposed zero-shot learning method for temporal FSE reconstruction. The results
demonstrate the success of our proposed method where NMSE and SSIM are
significantly increased and the artifacts are reduced.
