Defu Qiu1, Shengxiang Zhang2, Ying Liu3, Jianqing Zhu4, Lixin Zheng5. 1. Engineering Research Centre in Industrial Intellectual Techniques and Systems of Fujian Providence College of Engineering, Huaqiao University, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China. Electronic address: 17014084010@hqu.edu.cn. 2. Engineering Research Centre in Industrial Intellectual Techniques and Systems of Fujian Providence College of Engineering, Huaqiao University, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China. Electronic address: 1611422010@hqu.edu.cn. 3. Engineering Research Centre in Industrial Intellectual Techniques and Systems of Fujian Providence College of Engineering, Huaqiao University, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China. Electronic address: 18014084007@hqu.edu.cn. 4. Engineering Research Centre in Industrial Intellectual Techniques and Systems of Fujian Providence College of Engineering, Huaqiao University, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China. Electronic address: jqzhu@hqu.edu.cn. 5. Engineering Research Centre in Industrial Intellectual Techniques and Systems of Fujian Providence College of Engineering, Huaqiao University, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China. Electronic address: zlx@hqu.edu.cn.
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.
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.
Authors: Jennifer A Steeden; Michael Quail; Alexander Gotschy; Kristian H Mortensen; Andreas Hauptmann; Simon Arridge; Rodney Jones; Vivek Muthurangu Journal: J Cardiovasc Magn Reson Date: 2020-08-03 Impact factor: 5.364