Abstract
The widespread use of computed tomography (CT) in clinical practice has made the public focus on the cumulative radiation dose delivered to patients. Low-dose CT (LDCT) reduces the X-ray radiation dose, yet compromises quality and decreases diagnostic performance. Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques, which have achieved impressive results. However, most of these methods are directly performed on reconstructed LDCT images, in which some subtle structures and details are readily lost during the reconstruction procedure, and convolutional neural network (CNN)-based methods for raw LDCT projection data are rarely reported. To address this problem, we adopted an attention residual dense CNN, referred to as AttRDN, for LDCT sinogram denoising. First, it was aided by the attention mechanism, in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms. Then, the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram. Finally, the CT image was reconstructed using filtered back-projection. The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods. Importantly, it can prevent the loss of detailed information and has the potential for clinical application.
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Acknowledgements
The authors would also like to thank Dr. Cynthia McCollough, the Mayo Clinic, the American Association of Physicists in Medicine, and grant EB017095 and EB017185 from the National Institute of Biomedical Imaging and Bioengineering for providing the Low-Dose CT Grand Challenge dataset.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yin-Jin Ma, Peng Feng, Peng He, and Xiao-Dong Guo. The first draft of the manuscript was written by Yin-Jin Ma, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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This work was supported in part by the National Key R&D Program of China (Nos. 2016YFC0104609 and 2019YFC0605203). The Fundamental Research Funds for the Central Universities (Nos. 2019CDYGYB019 and 2020CDJ-LHZZ-075). And the Chongqing Basic Research and Frontier Exploration Project (No. cstc2020jcyj-msxmX0553).
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Ma, YJ., Ren, Y., Feng, P. et al. Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography. NUCL SCI TECH 32, 41 (2021). https://doi.org/10.1007/s41365-021-00874-2
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DOI: https://doi.org/10.1007/s41365-021-00874-2