Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning. (April 2020)

• Record Type: Journal Article
• Authors: Qiu, Defu; Zhang, Shengxiang; Liu, Ying; Zhu, Jianqing; Zheng, Lixin
• Is Part Of: Computer methods and programs in biomedicine. Volume 187(2020)
• Publication Date: 2020-04
• Subjects: Convolutional neural network -- Deep learning -- Low-resolution image -- Medical imaging -- Super resolution; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105059
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed an efficient medical image super-resolution method based on deep learning to assist the examination of knee osteoarthritis. Utiltzed the medical image super-resolution method of end-to-end learning to reduce the reconstruction time. Combined with the advantages of shallow super-resolution reconstruction network, the cascading convolution kernel is used to deepen the network, which effectively improves the image quality. Effectively solves the problem of low resolution and low definition of medical images. Abstract: Background and objective: With the rapid development of medical imaging and intelligent diagnosis, artificial intelligence methods have become a research hotspot of radiography processing technology in recent years. The low definition of knee magnetic resonance image texture seriously affects the diagnosis of knee osteoarthritis. This paper presents a super-resolution reconstruction method to address this problem. Methods: In this paper, we propose an efficient medical image super-resolution (EMISR) method, in which we mainly adopted three hidden layers of super-resolution convolution neural network (SRCNN) and a sub-pixel convolution layer of efficient sub-pixel convolution neural network (ESPCN). The addition of the efficient sub-pixel convolutional layer in the hidden layer and the small network replacement consisting of concatenated convolutions to address low-resolution images but not high-resolution images are important. The EMISR method also uses cascaded small convolution kernels to improve reconstruction speed and deepen the convolution neural network to improve reconstruction quality. Results: The proposed method is tested in the public dataset IDI, and the reconstruction quality of the algorithm is higher than that of the sparse coding-based network (SCN) method, the SRCNN method, and the ESPCN method (+ 2.306 dB, + 2.540 dB, + 1.089 dB improved); moreover, the reconstruction speed is faster than its counterparts (+ 4.272 s, + 1.967 s, and + 0.073 s improved). Conclusion: The experimental results show that our EMISR framework has improved performance and greatly reduces the number of parameters and training time. Furthermore, the reconstructed image presents more details, and the edges are more complete. Therefore, the EMISR technique provides a more powerful medical analysis in knee osteoarthritis examinations.