Christopher Man^{1,2}, Zheyuan Yi^{1,2}, Vick Lau^{1,2}, Jiahao Hu^{1,2}, Yujiao Zhao^{1,2}, Linfang Xiao^{1,2}, Alex T.L. Leong^{1,2}, and Ed X. Wu^{1,2}

^{1}Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, ^{2}Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

RAKI is recently proposed as a deep learning version of GRAPPA, which trains on auto-calibration signal (ACS) to estimate the missing k-space data. However, RAKI requires a larger amount of ACS for training and reconstruction due to its multiple convolutions which resulting in lower effective acceleration. In this study, we propose to incorporate the virtual conjugate coil and enhanced non-linearity into the RAKI framework to improve the noise resilience and artifact removal at high effective acceleration. The results demonstrate that such strategy is effective and robust at high effective acceleration and in presence of pathological anomaly.

In this study, we propose virtual-conjugate-coil RAKI (VCC-ELU-RAKI) to enable a robust and effective reconstruction at high effective acceleration. It incorporates extra encoding power by utilizing the characteristic of the conjugate symmetry and exponential linear-unit (ELU)

MR image data is complex-valued with the phase distribution depending on Bo field inhomogeneity and pulse sequence

In VCC-GRAPPA

For RAKI, we implemented a 3-layer 2D network according to the original paper. It consisted of 3 convolutional layers with kernel sizes of 5x2, 1x1 and 3x3 respectively and 2 non-linear activation functions. The network treated the zero-filled undersampled k-space as input with separate channels for real and imaginary components of the complex signal and outputs 2·(R-1) channels per coil, which were subsequently combined to form the complete k-space.

Regarding VCC, additional virtual coils were synthesized according to equation (1) by taking the complex conjugate symmetry of k-space data in both ACS and undersampled region. The virtual coils were then concatenated along the channel dimension of the network and standard RAKI reconstruction with additional virtual coils was performed. This takes advantage of the additional phase information provided by the conjugate symmetry to further improve the noise resilience in comparison to RAKI at high effective acceleration.

To further improve the reconstruction performance, we replaced ReLU activation function with ELU to enhance the non-linearity of the network for better convergence property

The NYU fastMRI T2 brain dataset

PSNR and NRMSE were used to quantitatively evaluate the reconstructed images. The proposed method was also evaluated with images containing pathological anomaly.

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DOI: https://doi.org/10.58530/2022/4355