Kevin M Johnson1. 1. Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Abstract
PURPOSE: To design and develop a series of ultrashort echo time k-space sampling schemes, termed radial-cones, which enables high sampling efficiency while maintaining compatibility with parallel imaging and compressed sensing reconstructions. THEORY AND METHODS: Radial-cones is a trajectory that samples three-dimensional (3D) k-space using a single base cone distributed along radial dimensions through a cost function-based optimization. Trajectories were generated for highly undersampled, short readout sampling and compared with 3D radial sampling in point spread function (PSF) analysis, digital and physical phantoms, and initial human volunteers. Parallel imaging reconstructions were evaluated with and without the use of compressed sensing-based regularization. RESULTS: Compared with 3D radial sampling, radial-cones reduced the peak value and energy of PSF aliasing. In both digital and physical phantoms, this improved sampling behavior corresponded to a reduction in the root mean square error with a further reduction using compressed sensing. A slight increase in noise and a corresponding increase in apparent resolution was observed with radial-cones. In in vivo feasibility testing, radial-cones reconstructed images have a markedly lower number of apparent artifacts. Ultimate gains in imaging performance were limited by off-resonance blurring. CONCLUSION: Radial-cones is an efficient non-Cartesian sampling scheme enabling short echo readout with a high level of compatibility with parallel imaging and compressed sensing. Magn Reson Med 77:1068-1081, 2017.
PURPOSE: To design and develop a series of ultrashort echo time k-space sampling schemes, termed radial-cones, which enables high sampling efficiency while maintaining compatibility with parallel imaging and compressed sensing reconstructions. THEORY AND METHODS: Radial-cones is a trajectory that samples three-dimensional (3D) k-space using a single base cone distributed along radial dimensions through a cost function-based optimization. Trajectories were generated for highly undersampled, short readout sampling and compared with 3D radial sampling in point spread function (PSF) analysis, digital and physical phantoms, and initial human volunteers. Parallel imaging reconstructions were evaluated with and without the use of compressed sensing-based regularization. RESULTS: Compared with 3D radial sampling, radial-cones reduced the peak value and energy of PSF aliasing. In both digital and physical phantoms, this improved sampling behavior corresponded to a reduction in the root mean square error with a further reduction using compressed sensing. A slight increase in noise and a corresponding increase in apparent resolution was observed with radial-cones. In in vivo feasibility testing, radial-cones reconstructed images have a markedly lower number of apparent artifacts. Ultimate gains in imaging performance were limited by off-resonance blurring. CONCLUSION: Radial-cones is an efficient non-Cartesian sampling scheme enabling short echo readout with a high level of compatibility with parallel imaging and compressed sensing. Magn Reson Med 77:1068-1081, 2017.
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