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Abstract #4652

Deep Scaled Domain Learning for Compressed MRI using Optional Scaling Transform

Satoshi ITO1 and Kohei SATO1

1Utsunomiya University, Utsunomiya, Japan

Image domain learning designed for image denoiser has superior performance when aliasing artifacts are incoherent; however, its performances will be degraded if the artifacts show small incoherency. In this work, a novel image domain learning CNN is proposed in which images are transformed to scaled space to improve the incoherency of artifacts. Simulation and experiments showed that the quality of obtained image was fairly improved especially for lower sampling rate and the quality was further improved by cascaded network. It was also shown that the resultant PSNR exceeded one of the transform learning method.

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