Imaging Science in Dentistry

  • 제50권4호
  • /
  • Pages.331-337
  • /
  • 2020
  • /
  • 2233-7822(pISSN)
  • /
  • 2233-7830(eISSN)
대한영상치의학회 (Korean Academy of Oral and Maxillofacial Radiology)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2020.08.01
  • 심사 : 2020.09.03
  • 발행 : 2020.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

키워드

참고문헌

  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. https://doi.org/10.1109/trpms.2018.2827239
  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. https://doi.org/10.1109/tmm.2019.2919431
  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. https://doi.org/10.1088/2057-1976/2/5/055010
  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. https://doi.org/10.1016/S0140-6736(18)31645-3
  6. 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.
  7. 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.
  8. 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. https://doi.org/10.1134/s0361768820030111
  9. 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. https://doi.org/10.1186/s13014-020-01630-3
  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. https://doi.org/10.5624/isd.2019.49.1.1
  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. https://doi.org/10.1109/TPAMI.2015.2439281

피인용 문헌

  1. Self-supervised CT super-resolution with hybrid model vol.138, 2020, https://doi.org/10.1016/j.compbiomed.2021.104775