Compressed sensing acceleration of radial 3-D alternating Look-Locker T 1 mapping

To determine how various compressed sensing (CS) models can accelerate alternating Look-Locker $T_1$ mapping.

An alternating Look-Locker acquisition was retrospectively accelerated by factors of 1-12. The data was reconstructed into 12 images with multiple CS models, which utilized combinations of spatial total variation, locally low-rank regularization, and subspace constraints. Complex non-linear least squares signal fitting was performed to obtain the $T_1$ maps. The accelerated $T_1$ maps were compared against the $T_1$ map of a full data reference reconstruction.

A subspace-constrained reconstruction model with spatial total variation and locally low-rank regularization outperformed all other models as measured by $T_1$ map normalized root mean squared error, structural similarity index, and normalized mean absolute deviation. The subspace constraint benefited models utilizing spatial total variation but, conversely, did not benefit models utilizing only locally low-rank regularization.

The radial 3-D alternating Look-Locker $T_1$ mapping acquisition was successfully accelerated by up to a factor of 12 with various CS models. The best-performing model was a subspace-constrained reconstruction, which utilized spatial total variation and locally low-rank regularization.