Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2217001(2022)
Super-Resolution Reconstruction of Magnetic Resonance Image Based on Deep Learning
Given the relatively small proportion of breast cancer in the overall image, which affects the accuracy of early breast cancer detection, this study proposes a wide residual-depth neural network based on convolution residual blocks to restore the high-resolution features of breast cancer magnetic resonance images. The proposed method adopts the combination of global and local residuals, allowing the top layer of the network to directly receive a substantial amount of low-frequency information. A convolution layer is added in front of each residual block for feature pre-extraction, and the sub-pixel convolution layer is used for up-sampling to complete the reconstruction of the low-resolution image. Experiments on the dataset with 260 samples and comparisons with other methods reveal that the proposed network outperforms bicubic interpolation and other deep learning methods in super-resolution of breast cancer magnetic resonance images.
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Mengxue Pan, Ning Qu, Yeru Xia, Deyong Yang, Hongyu Wang, Wenlong Liu. Super-Resolution Reconstruction of Magnetic Resonance Image Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2217001
Category: Medical Optics and Biotechnology
Received: Aug. 2, 2021
Accepted: Oct. 13, 2021
Published Online: Oct. 13, 2022
The Author Email: Liu Wenlong (liuwl@dlut.edu.cn)