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Abstract #4660

Parallel Imaging based on k-x Domain Interpolation using Deep Neural Networks

Hyungseob Shin1, Taejoon Eo1, Yohan Jun1, Taeseong Kim1, and Dosik Hwang1

1Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of

In this study, we compare two deep learning approaches to reconstruct multi-channel magnetic resonance (MR) images subsampled along phase-encoding (PE) direction. They are both based on the Fully-Connected (FC) layers but are performed in two different domains : Image domain, and k-x domain which is 1D inverse Fourier transformed (IFT) k-space. We demonstrate that the latter method shows superior performance to the former one in terms of removing the aliasing artifacts and recovering the details of MR images. The performance of the proposed method to the conventional MR reconstruction on image domain was qualitatively and quantitatively evaluated.

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