Very deep super-resolution for efficient cone-beam computed tomographic image restoration |
Hwang, Jae Joon
(Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University)
Jung, Yun-Hoa (Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University) Cho, Bong-Hae (Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University) Heo, Min-Suk (Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University) |
1 | Hatvani J, Horvath A, Michetti J, Basarab A, Kouame D, Gyongy M. Deep learning-based super-resolution applied to dental computed tomography. IEEE Trans Radiat Plasma Med Sci 2019; 3; 120-8. DOI |
2 | Yang W, Zhang X, Tian Y, Wang W, Xue J, Liao Q. Deep learning for single image super-resolution: a brief review. IEEE Trans Multimedia 2019; 21; 3106-21. DOI |
3 | Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, USA: 2016. p. 1646-54. |
4 | Biguri A, Dosanjh M, Hancock S, Soleimani M. TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction. Biomed Phys Eng Express 2016; 2: 055010. DOI |
5 | Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018; 392: 2388-96. DOI |
6 | Song Y, Zhang W, Zhang H, Wang Q, Xiao Q, Li Z, et al. Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning. Radiat Oncol 2020; 15: 192. DOI |
7 | Miao H, Zhao H, Gao F, Gong S. Implementation of FDK reconstruction algorithm in cone-beam CT based on the 3D Shepp-Logan model. 2009 2nd International Conference on Biomedical Engineering and Informatics; Tianjin, China: 2009. p. 1-5. |
8 | Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS); Belgaum, India: 2018. p. 92-9. |
9 | Zvezdakova AV, Kulikov DL, Zvezdakov SV, Vatolin DS. BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions. Program Comput Soft 2020; 46: 183-94. DOI |
10 | Reddy BV, Reddy PB, Kumar PS, Reddy AS. Lossless compression of medical images for better diagnosis. 2016 IEEE 6th International Conference on Advanced Computing (IACC); Bhimavaram. India: 2016. p. 404-8. |
11 | Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49; 1-7. DOI |
12 | Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 2016; 38: 295-307. DOI |