Yonghao Liu
ABSTRACT
High-quality Magnetic Resonance Imaging (MRI) provides an aid to reliable diagnosis. By processing medical images, doctors are able to view pathological features more easily; however, since pathological features are usually small in the early stages, we would like to obtain medical images that are as clear as possible so that doctors can observe relevant features and even finer textures. Therefore, applying image super-resolution technology to medical images can reconstruct high-resolution medical images without increasing hardware equipment, which helps doctors make better diagnoses of patients' conditions. The use of deep learning methods can effectively reconstruct high-resolution images. In this paper, RCAN network is applied to the medical image dataset IXI and formed a control experiment with SAN, EDSR and T2Net network, and RCAN achieves good results both in terms of data metrics and in terms of recovering images for analysis.
- Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M. 2017: Enhanced deep residual networks for single image super-resolution. In: CVPR.Google Scholar
- Ledig, C., Theis, L., Husz´ar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W. 2017: Photo-realistic single image superresolution using a generative adversarial network. In: CVPR.Google Scholar
- Dong, C., Loy, C.C., Tang, X. 2016: Accelerating the super-resolution convolutional neural network. In: ECCV.Google Scholar
- Dumoulin, V., Shlens, J., Kudlur, M. 2017: A learned representation for artistic style.In: ICLR.Google Scholar
- Shi, W., Caballero, J., Husz´ar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z. 2016: Real-time single image and video super-resolution using an efficientn sub-pixel convolutional neural network. In: CVPR.Google Scholar
- Tai, Y., Yang, J., Liu, X. 2017: Image super-resolution via deep recursive residual network. In: CVPR.Google Scholar
- Tai, Y., Yang, J., Liu, X., Xu, C. 2017: Memnet: A persistent memory network for image restoration. In: ICCV.Google Scholar
- Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. 2018: Memnet: Image Super-Resolution Using Very Deep Residual Channel Attention Networks. In: ECCV.Google Scholar
Index Terms
- RCAN based MRI super-resolution with applications
Recommendations
3D Brain MRI Reconstruction based on 2D Super-Resolution Technology
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)Magnetic resonance imaging (MRI) is one of the most important diagnostic imaging methods, which is widely used in diagnosis and image-guided therapy, especially imaging diagnosis of the brain. However, MRI images have the characteristics of low resolution,...
Super resolution in CT
The general framework of super resolution in computed tomography CT system is introduced. Two data acquisition ways before or after the reconstruction respectively are described. Three models including the sinogram model, the in-plane model and the z-...
Dual Arbitrary Scale Super-Resolution for Multi-contrast MRI
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023AbstractLimited by imaging systems, the reconstruction of Magnetic Resonance Imaging (MRI) images from partial measurement is essential to medical imaging research. Benefiting from the diverse and complementary information of multi-contrast MR images in ...
Comments