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- W3005472550 abstract "Deep learning approaches to accelerated MRI take a matrix of sampled Fourier-space lines as input and produce a spatial image as output. In this work we show that by careful choice of the offset used in the sampling procedure, the symmetries in k-space can be better exploited, producing higher quality reconstructions than given by standard equally-spaced samples or randomized samples motivated by compressed sensing." @default.
- W3005472550 created "2020-02-14" @default.
- W3005472550 creator A5059388072 @default.
- W3005472550 date "2019-12-02" @default.
- W3005472550 modified "2023-09-27" @default.
- W3005472550 title "Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry" @default.
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