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In order to improve the accuracy of surgical analysis and clinical diagnosis, it is very important to obtain brain structure images. And magnetic resonance imaging (MRI) is one of the most commonly used methods. Moreover, High-Resolution (HR) MR image with smaller pixel size provides more important structure and texture details [1] and helps early diagnosis and subsequent analysis. But in fact, it is relatively difficult to obtain high-quality MR images due to many factors, such as hardware equipment, imaging time, required signal to noise ratio (Signal to Noise Ratio, SNR) and motion artifacts, etc. Generally speaking, brain MR images are often obtained with thick slice thickness and lower image quality to reduce the scanning cost and sampling time. However, this is not conducive to further medical analysis. For decades, Super- Resolution (SR) related technologies have been used to improve the quality of MR images to restore important structural information and facilitate clinical diagnosis. Also, as deep learning (DL) develops significantly nowadays, DL-based methods are widely applied to SR issues. Therefore, we propose a convolution neural network (CNN) SR method called Channel Splitting Edge-guided Residual Network (CSERN). Besides, we combine our method with a novel accelerating imaging method called Single-frequency Excitation WideBand (SE-WB) MRI and design a loss function to achieve higher performance on several index such as structural similarity index (SSIM) and Peak signal-to-noise ratio (PSNR).
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