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3D self-navigated multi-echo stack-of-radial Dixon sequence has been used to quantify fat and R2* with free-breathing acquisitions. To compensate motion, motion-resolved compressed sensing (CS) uses self-navigation for data binning, and applies sparsity constraint along the dimension of motion states. However, this approach does not explicitly model non-rigid motion in the liver. In addition to artifacts caused by respiratory motion, hardware imperfection such as gradient nonlinearity can lead to artifacts and affect the image quality. In this work, use a phase-preserving beamforming-based coil sensitivity estimation method and non-rigid motion compensation in a CS model to improve free-breathing PDFF and R2* quantification.
Shih et al. (Wed,) studied this question.