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Abstract #3440

Parallel MRI Reconstruction via Residual UNet with Joint Consideration of k-Space and Image Space

Xiaoxia Zhang1, Xiaopeng Zong1, Yong Chen1, Zhenghan Fang1, and Pew-Thian Yap1
1Department of Radiology, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

We propose a strategy based on residual U-Net to reconstruct MR images from undersampled multichannel data by considering both k-space and image space. Our method first imputes the missing data points in k-space by utilizing the intrinsic relationships among channels. Then, the image reconstructed from the imputed k-space data is fed to another network for spatial detail refinement. Our method does not necessarily require auto-calibration signal (ACS) and is hence less susceptible to motion-induced inconsistency between the ACS and the undersampled data. Comprehensive evaluation indicates that our method yields images with superior perceptual details.

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