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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Change history
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.”
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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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