Abstract
Clear and high-resolution medical computed tomography (CT) images are of great significance for early medical diagnosis. In practice, there are many cases of medical CT images that lead to poor image quality which complicates the diagnosis process. This paper proposes a new type of neural network that enables low-resolution images to reconstruct high-resolution images and aids in the early diagnosis of medical CT images. The network mainly consists of a low-level feature extraction module, a cyclic feature concentration block, and a reconstruction module. First, the underlying feature extraction module extracts the effective feature information of the low-resolution image. Then, the cyclic feature concentration block further extracts the feature information. Finally, the reconstruction module reconstructs the high-definition image. It can be seen from the experiment results that the method proposed in this paper achieved the best results on two objective indicators.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kak, A.C., Slaney, M., Wang, G.: Principles of computerized tomographic imaging. Med. Phys. 29(1), 107 (2002)
Miglioretti, D.L., Johnson, E., Williams, A., et al.: The use of computed tomography in pediatrics and the associated radiation exposure and estimated cancer risk. JAMA Pediatr. 167(8), 700–707 (2013)
La Rivière, P.J., Bian, J., Vargas, P.A.: Penalized-likelihood sinogram restoration for computed tomography. IEEE Trans. Med. Imaging 25(8), 1022–1036 (2006)
Jiang, M., Wang, G., Skinner, M.W., et al.: Blind deblurring of spiral CT images. IEEE Trans. Med. Imaging 22(7), 837–845 (2003)
Poot, D.H.J., Van Meir, V., Sijbers, J.: General and efficient super-resolution method for multi-slice MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 615–622. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15705-9_75
Shi, W., et al.: Cardiac image super-resolution with global correspondence using multi-atlas patchmatch. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 9–16. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40760-4_2
Odille, F., Bustin, A., Chen, B., Vuissoz, P.-A., Felblinger, J.: Motion-corrected, super-resolution reconstruction for high-resolution 3D cardiac cine MRI. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 435–442. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_52
Zhang, R., Thibault, J.B., Bouman, C.A., et al.: Model-based iterative reconstruction for dual-energy X-ray CT using a joint quadratic likelihood model. IEEE Trans. Med. Imaging 33(1), 117–134 (2013)
Yu, Z., Thibault, J.B., Bouman, C.A., et al.: Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization. IEEE Trans. Image Process. 20(1), 161–175 (2010)
Thibault, J.B., Sauer, K.D., Bouman, C.A., et al.: A three-dimensional statistical approach to improved image quality for multislice helical CT. Med. Phys. 34(11), 4526–4544 (2007)
Yang, J., Wright, J., Huang, T.S., et al.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Zhang, Y., Wu, G., Yap, P.T., et al.: Reconstruction of super-resolution lung 4D-CT using patch-based sparse representation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 925–931. IEEE (2012)
Dong, W., Zhang, L., Shi, G., et al.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Ledig, C., Theis, L., Huszár, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25
Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723–731 (2018)
Wang, G., Kalra, M., Orton, C.G.: Machine learning will transform radiology significantly within the next 5 years. Med. Phys. 44(6), 2041–2044 (2017)
Chen, Y., Shi, F., Christodoulou, Anthony G., Xie, Y., Zhou, Z., Li, D.: Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 91–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_11
Yu, H., Liu, D., Shi, H., et al.: Computed tomography super-resolution using convolutional neural networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3944–3948. IEEE (2017)
Wolterink, J.M., Leiner, T., Viergever, M.A., et al.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017)
Mardani, M., Gong, E., Cheng, J.Y., et al.: Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans. Med. Imaging 38(1), 167–179 (2018)
You, C., Li, G., Zhang, Y., et al.: CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE). IEEE Trans. Med. Imaging 39(1), 188–203 (2019)
Chi, J., Zhang, Y., Yu, X., et al.: Computed tomography (CT) image quality enhancement via a uniform framework integrating noise estimation and super-resolution networks. Sensors 19(15), 3348 (2019)
Shi, W., Caballero, J., Huszár, F., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Dong, C., Loy, C.C., He, K., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Acknowledgment
This work is supported in part by grants from the China Postdoctoral Science Foundation No. 2017M611905, Suzhou Industrial Technology Innovation Special Project (Liveli-hood Technology)—Technology Demonstration Project No. SS201701 No. SYSD2019152, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, X., Jia, J. (2020). Medical CT Image Super-Resolution via Cyclic Feature Concentration Network. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-60633-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60632-9
Online ISBN: 978-3-030-60633-6
eBook Packages: Computer ScienceComputer Science (R0)