  Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown
potential in accelerated MRI in a scan-specific scenario, which enabled
high-quality reconstructions without access to a large training dataset. ZS-SSL
has been further combined with the subspace model to accelerate 2D T2-shuffling
acquisitions. In this work, we propose a parallel network framework and
introduce an attention mechanism to improve subspace-based zero-shot
self-supervised learning and enable higher acceleration factors. We name our
method SubZero and demonstrate that it can achieve improved performance
compared with current methods in T1 and T2 mapping acquisitions.
