Abstract
Low-dose CT imaging has been applied in modern medical practice, because it can greatly reduce the radiation for patients. However, with the decrease of radiation dose, noise level is getting much higher. The widely used traditional filtered back-projection (FBP) method is not competent for dealing with the low-dose CT projection data because it lacks of consideration on noise characteristic. Therefore, the statistical iteration algorithm which can consider the noise characteristics is gradually taken into account. But the slow convergence speed and heavy time-consuming limits its application in clinic. Many researchers are also working on the state-of-the-art iterative algorithms, so that they can adapt to low dose CT reconstruction and greatly reduce time. In this paper, we first analyzed the noise characteristics of low-dose CT projection. Then, considering the statistical property of noisy sinogram and the superiority and inferiority of the iterative algorithm, we proposed an adjustable dynamic self-adapting OSEM method (ADSA-OSEM). This method combines variable subset strategy with the least squares merit algorithm applied to maximum likelihood function on OSEM algorithm instead of fixed subsets of traditional OSEM method. A simulation study is performed to test the effectiveness and advantage of the proposed method by comparing with FBP and traditional OSEM method. Through flexible adjustment of the adaptive parameters, results show the new method has greater performance in reconstructed image quality with fewer iterations, the granularity noise and streak-like artifacts could be well suppressed.
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13 January 2019
In the originally published version of chapters 21 and 33 the name of the author Jinhua Sheng was incorrectly spelled as “Jinghua Sheng.” It was corrected to “Jinhua Sheng.”
References
Brenner, D.J., Hall, E.J., Brenner, D.J., et al.: Computed tomography—an increasing source of radiation exposure. N. Engl. J. Med. 357(22), 2277–2284 (2007)
Prasad, K.N., Cole, W.C., Haase, G.M.: 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)
Kalender, W.A., Wolf, H., Suess, C., et al.: Dose reduction in CT by anatomically adapted tube current modulation: principles and first results. In: Krestin, G.P., Glazer, G.M. (eds.) Advances in CT IV. Springer, Heidelberg (1998). https://doi.org/10.1007/978-3-642-72195-3_4
Yazdi, M., Beaulieu, L.: Artifacts in spiral X-ray CT scanners: problems and solutions. Int. J. Biol. Med. Sci. 4(3), 135–139 (2008)
Deledalle, C.A., Tupin, F., Denis, L.: Poisson NL means: unsupervised non local means for Poisson noise. In: IEEE International Conference on Image Processing, pp. 801–804. IEEE (2010)
Dong, Z., Huang, J., Bian, Z., et al.: A simple low-dose X-ray CT simulation from high-dose scan. IEEE Trans. Nucl. Sci. 62(5), 2226–2233 (2015)
Lu, H., Hsiao, I.T., Li, X., et al.: Noise properties of low-dose CT projections and noise treatment by scale transformations. In: Nuclear Science Symposium Conference Record, vol. 3, pp. 1662–1666. IEEE (2001)
Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imaging 13(4), 601–609 (2002)
Sheng J, Liu D.: An improved maximum likelihood approach to image reconstruction using ordered subsets and data subdivisions. In: Design & Test Symposium. IEEE Xplore, pp. 43–46 (2004)
Hsieh, J.: Computed tomography: principles, design, artifacts, and recent advances. SPIE, Bellingham (2009)
Shepp, L.A., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1(2), 113–122 (1982)
Hu, Z., Zhang, Y., Liu, J., et al.: A feature refinement approach for statistical interior CT reconstruction. Phys. Med. Biol. 61(14), 5311–5334 (2016)
Byrne, C.L.: Block-iterative methods for image reconstruction from projections. IEEE Trans. Image Process. 5(5), 792–794 (1996)
Chen, H., Zhang, Y., Zhang, W., et al.: Low-dose CT via convolutional neural network. Biomed. Opt. Express 8(2), 679–694 (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)
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Sheng, J., Chen, B., Wang, B., Ma, Y., Liu, Q., Liu, W. (2018). An Adjustable Dynamic Self-Adapting OSEM Approach to Low-Dose X-Ray CT Image Reconstruction. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_33
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DOI: https://doi.org/10.1007/978-3-030-04224-0_33
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