Data-driven optimization of sampling patterns for MR brain T1ρ mapping

Purpose: The goal of this study was to apply a fast data-driven optimization algorithm, called bias-accelerated subset selection, for MR brain T-1 rho mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D-T-1 rho MRI. Methods: Five healthy volunteers were recruited, and fully sampled Cartesian 3D-T-1 rho MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low-rank plus sparse with spatial finite differences, and low rank. The performance of images and T-1 rho maps using PD-SP and VD-SP and their optimized sampling patterns (PD-OSP and VD-OSP) were compared to the fully sampled reference using normalized root mean square error (NRMSE). Results: The VD-OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.078) and the PD-OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.079) at the highest acceleration factors (AF = 30) showed the largest improvement compared to the respective nonoptimized SPs (VD NRMSE = 0.087 and PD NRMSE = 0.149). Prospective undersampling was tested at AF = 4, with VD-OSP NRMSE = 0.057 versus PD-OSP NRMSE = 0.060, with optimized sampling performing better that input PD or VD sampling. For brain T-1 rho mapping, the VD-OSP with low rank reconstruction for AFs <10 and VD-OSP with spatiotemporal finite differences for AFs >10 perform better. Conclusions: The study demonstrated that the appropriate use of data-driven optimized sampling and suitable compressed sensing reconstruction technique can be employed to potentially accelerate 3D T-1 rho mapping for brain imaging applications.

Automated summary

The purpose of this study was to apply a fast data-driven optimization algorithm for MR brain T-1 rho mapping to generate optimized sampling patterns for compressed sensing reconstruction. The study compared different sampling patterns and found that the use of optimized sampling patterns and suitable compressed sensing techniques can potentially accelerate 3D T-1 rho mapping for brain imaging applications.