Abstract
Low-dose computer tomography (LDCT) has been widely used in medical diagnosis yet suffered from spatial resolution loss and artifacts. Numerous methods have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted; (2) features with fixed-resolution lose the attention to multi-scale information; (3) single super-resolution module fails to balance details reconstruction and noise removal. Therefore, we propose an LDCT image super-resolution network consisting of a dual-guidance feature distillation backbone for elaborate visual feature extraction, and a dual-path content communication head for artifacts-free and details-clear CT reconstruction. Specifically, the dual-guidance feature distillation backbone is composed of a dual-guidance fusion module (DGFM) and a sampling attention block (SAB). The DGFM guides the network to concentrate the feature representation of the 3D inter-slice information in the region of interest (ROI) by introducing the average CT image and segmentation mask as complements of the original LDCT input. Meanwhile, the elaborate SAB utilizes the essential multi-scale features to capture visual information more relative to edges. The dual-path reconstruction architecture introduces the denoising head before and after the super-resolution (SR) head in each path to suppress residual artifacts, respectively. Furthermore, the heads with the same function share the parameters so as to efficiently improve the reconstruction performance by reducing the amount of parameters. The experiments compared with 6 state-of-the-art methods on 2 public datasets prove the superiority of our method. The code is made available at https://github.com/neu-szy/dual-guidance_LDCT_SR.
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References
Bruno, D.M., Samit, B.: Distance-driven projection and backprojection in three dimensions. Phys. Med. Biol. 49(11), 2463–2475 (2004)
Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017)
Chen, Y., Zheng, Q., Chen, J.: Double paths network with residual information distillation for improving lung CT image super resolution. Biomed. Sig. Process. Control 73, 103412 (2022)
Chi, J., Sun, Z., Wang, H., Lyu, P., Yu, X., Wu, C.: CT image super-resolution reconstruction based on global hybrid attention. Comput. Biol. Med. 150, 106112 (2022)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11065–11074 (2019)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
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
Hou, H., Jin, Q., Zhang, G., Li, Z.: Ct image quality enhancement via a dual-channel neural network with jointing denoising and super-resolution. Neurocomputing 492, 343–352 (2022)
Huang, Y., Li, S., Wang, L., Tan, T., et al.: Unfolding the alternating optimization for blind super resolution. Adv. Neural. Inf. Process. Syst. 33, 5632–5643 (2020)
Huang, Y., Wang, Q., Omachi, S.: Rethinking degradation: radiograph super-resolution via AID-SRGAN. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) Machine Learning in Medical Imaging, MLMI 2022. LNCS, vol. 13583, pp. 43–52. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21014-3_5
Irani, M., Peleg, S.: Super resolution from image sequences. In: 1990 Proceedings of the 10th International Conference on Pattern Recognition, vol. 2, pp. 115–120. IEEE (1990)
Irani, M., Peleg, S.: Improving resolution by image registration. Graph. Models Image Process. (CVGIP) 53(3), 231–239 (1991)
Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 466–467 (2020)
Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Sig. Process. 29(6), 1153–1160 (1981)
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)
Li, B., et al.: Diagnostic value and key features of computed tomography in coronavirus disease 2019. Emerg. Microbes Infect. 9(1), 787–793 (2020)
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using Swin transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1833–1844 (2021)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)
Ma, C., Rao, Y., Lu, J., Zhou, J.: Structure-preserving image super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7898–7911 (2021)
Prasad, K., Cole, W., Haase, G.: Radiation protection in humans: extending the concept of as low as reasonably achievable (ALARA) from dose to biological damage. Br. J. Radiol. 77(914), 97–99 (2004)
Ramani, S., Fessler, J.A.: A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction. IEEE Trans. Med. Imaging 31(3), 677–688 (2012)
Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996)
Smith, P.: Bilinear interpolation of digital images. Ultramicroscopy 6(2), 201–204 (1981)
Stark, H., Oskoui, P.: High-resolution image recovery from image-plane arrays, using convex projections. JOSA A 6(11), 1715–1726 (1989)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)
Veronesi, G., et al.: Recommendations for implementing lung cancer screening with low-dose computed tomography in Europe. Cancers 12(6), 1672 (2020)
Yin, X., et al.: Domain progressive 3D residual convolution network to improve low-dose CT imaging. IEEE Trans. Med. Imaging 38(12), 2903–2913 (2019)
Zeng, D., et al.: A simple low-dose X-ray CT simulation from high-dose scan. IEEE Trans. Nucl. Sci. 62(5), 2226–2233 (2015)
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480–2495 (2020)
Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant 61901098, 61971118, Science and Technology Plan of Liaoning Province 2021JH1/10400051.
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Chi, J., Sun, Z., Zhao, T., Wang, H., Yu, X., Wu, C. (2023). Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_10
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