Fast Compressed Sensing of 3D Radial T1 Mapping with Different Sparse and Low-Rank Models

Abstract

Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T1 relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T1 maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping

“…Previous work has accelerated the VFA protocol using sparse sampling techniques with compressed-sensing reconstruction. 29,[58][59][60][61] It was shown that a 1-mm isotropic multi-echo MT sat protocol using a compressed-sensing VFA protocol could be acquired in 15:40 (min:s) using a 4-times undersampling and in 10:30 with 6-times undersampling factor. 29 This produced high-quality quantitative maps in which metric variance increased with acceleration factor, with minimal acceleration-induced bias.…”