Linfang Xiao^{1,2}, Yilong Liu^{1,2}, Zheyuan Yi^{1,2}, Yujiao Zhao^{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, China, ^{2}Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Recently, deep learning methods have shown superior performance on image reconstruction and noise suppression by implicitly yet effectively learning prior information. However, end-to-end deep learning methods face the challenge of potential numerical instabilities and require complex application specific training. By taking advantage of the multichannel spatial encoding (as exploited by conventional parallel imaging reconstruction) and prior information (exploited by deep learning methods), we propose to embed a deep learning module into the iterative low-rank matrix completion based image reconstruction. Such strategy significantly suppresses the noise amplification and accelerates iteration convergence without image blurring.

For LR reconstruction demonstration, we use the classic simultaneous autocalibrating and k-space estimation

The deep learning prior module builds a nonlinear mapping between the fully-sampled reference image and the fully-sampled noise corrupted image. It is trained on U-Net

In this study, 3T T1w 3D GRE brain data with 1mm isotropic resolution from Calgary-Campinas Public Brain MR Database

LR-DL was evaluated using retrospectively undersampled k-space with varying undersampling patterns and acceleration factors. The performance of both LR (SAKE) and LR-DL was evaluated by reconstructed images, error maps and their NRMSE, PSNR and SSIM values.

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**Figure
1** (**A**) Diagram of iterative low-rank reconstruction with
an embedded deep learning prior module (LR-DL). (**B**) The deep learning prior module trained on U-Net with information
multi-distillation block (IMDB). The U-Net contains four scales with 2×2
strided convolution (SConv) downscaling and 2×2 transposed convolution (TConv)
upscaling for each. The number of channels for U-Net from the first to the
fourth scale is 64,128, 256 and 512, respectively. The kernel size is 3×3 for
all convolutions except for IMDBs. IMDB contains three 3×3 convolutions
followed by a Leaky ReLU.

**Figure 3** Typical reconstructions of 6-channel T1w GRE data from three different slices at R=3 along PE direction (undersampling pattern identical to that in Figure 2). LR-DL consistently demonstrated better performance on recovering high-frequency information and suppressing noise amplification.

DOI: https://doi.org/10.58530/2022/4347