SSL-QALAS: Self-Supervised Learning for Multiparametric Quantitative MRI Using QALAS

The 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) has been developed and used for acquiring high-resolution T1, T2, and PD maps from five measurements. The dictionary matching method can be used for generating quantitative maps from the acquired multi-contrast images; however, it requires an external dictionary, which needs to be pre-calculated and voxel-by-voxel fitting is computationally demanding. In this study, we propose to generate multiple quantitative maps including T1, T2, PD, and inversion efficiency (IE) maps using self-supervised learning from 3D-QALAS measurements (i.e., SSL-QALAS) for rapid, accurate, and dictionary-free multiparametric fitting.