Learning a variational network for reconstruction of accelerated MRI data

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Abstract

To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

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Most cited references 33

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Compressed sensing

D.L. Donoho (2006)

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Learning a variational network for reconstruction of accelerated MRI data

Michael P Recht, Daniel Sodickson, Thomas Pock … (2018)

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Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays.

Daniel Sodickson, Warren J. Manning (1997)

SiMultaneous Acquisition of Spatial Harmonics (SMASH) is a new fast-imaging technique that increases MR image acquisition speed by an integer factor over existing fast-imaging methods, without significant sacrifices in spatial resolution or signal-to-noise ratio. Image acquisition time is reduced by exploiting spatial information inherent in the geometry of a surface coil array to substitute for some of the phase encoding usually produced by magnetic field gradients. This allows for partially parallel image acquisitions using many of the existing fast-imaging sequences. Unlike the data combination algorithms of prior proposals for parallel imaging, SMASH reconstruction involves a small set of MR signal combinations prior to Fourier transformation, which can be advantageous for artifact handling and practical implementation. A twofold savings in image acquisition time is demonstrated here using commercial phased array coils on two different MR-imaging systems. Larger time savings factors can be expected for appropriate coil designs.