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Japanese Journal of Radiology

, Volume 37, Issue 1, pp 73–80 | Cite as

Improvement of image quality at CT and MRI using deep learning

  • Toru HigakiEmail author
  • Yuko Nakamura
  • Fuminari Tatsugami
  • Takeshi Nakaura
  • Kazuo Awai
Invited Review

Abstract

Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as “noise and artifact reduction”, “super resolution” and “image acquisition and reconstruction”. For each category, we present and outline the features of some studies.

Keywords

Deep learning Image quality improvement Computed tomography Magnetic resonance imaging 

Notes

Funding

Kazuo Awai received following research grant: Research Grant, Canon Medical systems, paid to the institution. Research Grant, Hitachi, paid to the institution.

Compliance with ethical standards

Conflict of interest

The other authors declare that they have no conflict of interest.

Ethical statement

None.

References

  1. 1.
    Giger ML. Machine learning in medical imaging. J Am Coll Radiol JACR. 2018.  https://doi.org/10.1016/j.jacr.2017.12.028.Google Scholar
  2. 2.
    Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018.  https://doi.org/10.1148/radiol.2018171820.Google Scholar
  3. 3.
    Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017.  https://doi.org/10.1148/rg.2017160130.Google Scholar
  4. 4.
    Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018.  https://doi.org/10.1007/s11604-018-0726-3.Google Scholar
  5. 5.
    Noguchi T, Higa D, Asada T, Kawata Y, Machitori A, Shida Y, et al. Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences. Jpn J Radiol. 2018.  https://doi.org/10.1007/s11604-018-0779-3.Google Scholar
  6. 6.
    Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017.  https://doi.org/10.1148/rg.2017170077.Google Scholar
  7. 7.
    Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T, et al. Machine learning-based texture analysis of contrast-enhanced MR imaging to differentiate between glioblastoma and primary central nervous system lymphoma. Magn Reson Med Sci. 2018.  https://doi.org/10.2463/mrms.mp.2017-0178.Google Scholar
  8. 8.
    Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. Trans Image Proc. 2017.  https://doi.org/10.1109/tip.2017.2662206.CrossRefGoogle Scholar
  9. 9.
    Cavigelli L, Hager P, Benini L. CAS-CNN: a deep convolutional neural network for image compression artifact suppression. Int Jt Conf Neural Netw (IJCNN). 2017;2017:752–9.Google Scholar
  10. 10.
    Svoboda P, Hradis M, Barina D, Zemcik P. Compression artifacts removal using convolutional neural networks. J WSCG. 2016;24(2):63–72.Google Scholar
  11. 11.
    Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016.  https://doi.org/10.1109/TPAMI.2015.2439281.Google Scholar
  12. 12.
    Freeman WT, Jones TR, Pasztor EC. Example-based super-resolution. IEEE Comput Graph Appl. 2002.  https://doi.org/10.1109/38.988747.Google Scholar
  13. 13.
    Iizuka S, Simo-Serra E, Ishikawa H. Let there be color! ACM Trans Graph. 2016.  https://doi.org/10.1145/2897824.2925974.Google Scholar
  14. 14.
    Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior. 2017. arXiv:1711.10925v2.
  15. 15.
    Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017.  https://doi.org/10.1364/boe.8.000679.Google Scholar
  16. 16.
    Du W, Chen H, Wu Z, Sun H, Liao P, Zhang Y. Stacked competitive networks for noise reduction in low-dose CT. PLoS One. 2017.  https://doi.org/10.1371/journal.pone.0190069.Google Scholar
  17. 17.
    Kang E, Min J, Ye JC. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017.  https://doi.org/10.1002/mp.12344.Google Scholar
  18. 18.
    Jiang D, Dou W, Vosters L, Xu X, Sun Y, Tan T. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Jpn J Radiol. 2018.  https://doi.org/10.1007/s11604-018-0758-8.Google Scholar
  19. 19.
    Zhang Y, Yu H. Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imaging. 2018.  https://doi.org/10.1109/TMI.2018.2823083.Google Scholar
  20. 20.
    Kim KH, Park SH. Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI. Magn Reson Imaging. 2017.  https://doi.org/10.1016/j.mri.2016.11.020.Google Scholar
  21. 21.
    Hauptmann A, Arridge S, Lucka F, Muthurangu V, Steeden JA. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med. 2018.  https://doi.org/10.1002/mrm.27480.Google Scholar
  22. 22.
    Umehara K, Ota J, Ishida T. Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging. 2017.  https://doi.org/10.1007/s10278-017-0033-z.Google Scholar
  23. 23.
    Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol. 2018.  https://doi.org/10.1088/1361-6560/aacdd4.Google Scholar
  24. 24.
    Liu C, Wu X, Yu X, Tang Y, Zhang J, Zhou J. Fusing multi-scale information in convolution network for MR image super-resolution reconstruction. Biomed Eng Online. 2018.  https://doi.org/10.1186/s12938-018-0546-9.Google Scholar
  25. 25.
    Wu D, Kim K, El Fakhri G, Li Q. Iterative low-dose CT reconstruction with priors trained by artificial neural network. IEEE Trans Med Imaging. 2017.  https://doi.org/10.1109/tmi.2017.2753138.Google Scholar
  26. 26.
    Jin KH, McCann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. 2017.  https://doi.org/10.1109/tip.2017.2713099.Google Scholar
  27. 27.
    Kida S, Nakamoto T, Nakano M, Nawa K, Haga A, Kotoku J, et al. Cone beam computed tomography image quality improvement using a deep convolutional neural network. Cureus. 2018.  https://doi.org/10.7759/cureus.2548.Google Scholar
  28. 28.
    Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018.  https://doi.org/10.1038/nature25988.Google Scholar
  29. 29.
    Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC. Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med. 2018.  https://doi.org/10.1002/mrm.27106.Google Scholar
  30. 30.
    Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, Samann P, et al. q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging. 2016.  https://doi.org/10.1109/tmi.2016.2551324.Google Scholar
  31. 31.
    Higaki T, Nishimaru E, Nakamura Y, Tatsugami F, Zhou J, Yu Z, et al. Radiation dose reduction in CT using deep learning based reconstruction (DLR): a phantom study. In: Proceeding of the 24th European congress of radiology. 2018.  https://doi.org/10.1594/ecr2018/c-1656.
  32. 32.
    Nakamura Y, Higaki T, Tatsugami F, Zhou J, Yu Z, Akino N, et al. Deep learning based reconstruction at CT: initial clinical trial targeting hypovascular hepatic metastases. Radiol Artif Intell. 2018. (Accepted).Google Scholar
  33. 33.
    Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, et al. Improvement of image quality at coronary CT angiography by using a deep learning based reconstruction. Eur Radiol. 2018. (Accepted).Google Scholar
  34. 34.
    Touch M, Clark DP, Barber W, Badea CT. A neural network-based method for spectral distortion correction in photon counting X-ray CT. Phys Med Biol. 2016.  https://doi.org/10.1088/0031-9155/61/16/6132.Google Scholar
  35. 35.
    Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y, et al. Deep embedding convolutional neural network for synthesizing CT image from T1-weighted MR image. Med Image Anal. 2018.  https://doi.org/10.1016/j.media.2018.03.011.Google Scholar

Copyright information

© Japan Radiological Society 2018

Authors and Affiliations

  1. 1.Department of Diagnostic Radiology, Graduate School of Biomedical and Health ScienceHiroshima UniversityHiroshimaJapan
  2. 2.Department of Diagnostic Radiology, Graduate School of Medical SciencesKumamoto UniversityKumamotoJapan

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