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Abstract #1750

Multi-channel and multi-group-based CNN Image Reconstruction using Equal-sized Multi-resolution Image Decomposition

Shohei Ouchi1 and Satoshi ITO1
1Utsunomiya University, Utsunomiya, Japan

CNN based Compressed Sensing reconstruction has attracted much attention. It is difficult to restore higher spatial frequency components even with CNN-CS. We proposed a new transformed image domain CNN-CS method based on equal-sized multi-resolution image decomposition (eFREBAS transform). The eFREBAS transform is multi resolution analysis method that divide the image into equal-sized sub-images. This CNN consisting of three U-Net CNNs, each with multi-channel input and output reconstructs MR phase varied images in frequency band-to-band through estimating artifact-free sub-images from under-sampled sub-images. Reconstruction experiments showed that eFREBAS-CNN could reconstruct sharp images that have strong phase variation.

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