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Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework.
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image reconstruction from the undersampled k-space data.
Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction
However, the two main limitations of SPARKLING are first that the optimal target sampling density is unknown and thus a user-defined parameter and second that this sampling pattern generation remains disconnected from MR image reconstruction thus from the optimization of image quality.
