Inversion recovery (IR) based single-shot approaches have become popular for rapid T1 mapping. Due to the highly accelerated nature of the acquisition, it is challenging to generate high quality contrast images and T1 maps from this dataset. To tackle this problem, we present a non-local low rank regularization model that is inspired by block matching approaches. For a given relaxation signal, we identify the top L similar relaxation signals within a spatial neighborhood and constrain them to have a low rank. We demonstrate this approach in single-shot high-resolution radial steady-state-free-precession (SSFP) brain and abdomen imaging.
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