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Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry

2019-12-02 · Aaron Defazio

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.

📄 PDF Abstract BibTeX arXiv:1912.01101

Code (2)

Kuga23/DL-fastMRI pytorch
facebookresearch/fastMRI pytorch

Tasks

compressed sensing

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