Joint image reconstruction and sensitivity estimation in SENSE (JSENSE)

By Leslie Ying, J. Sheng. Magnetic Resonance in Medicine, 2007

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Fast ℓ1-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime (2012, background): “estimation of the images and the sensitivity maps [13], [14].”
Accelerating SENSE using compressed sensing (2009, background): “It is known that inaccurate sensitivity proﬁles directly cause aliasing artifacts in basic SENSE (22).”
MODEL-BASED IMAGE RECONSTRUCTION FOR MRI (2010, background): “These differences can degrade the reconstructed image, particularly for non-Cartesian trajectories with long readout durations.”
ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA (2014, background): “[10]) computes a solution in the null space of W−I , while SENSE computes a solution in the subspace spanned by the coil sensitivities (Eq.”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “More recent approaches jointly estimate coil sensitivity profiles during the image reconstruction process [9, 10], but for the rest of this manuscript, we assume that sensitivity maps were precomputed.”