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Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging

Abstract : In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram-discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01685727
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, January 16, 2018 - 4:30:15 PM
Last modification on : Monday, August 17, 2020 - 2:20:04 PM

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Jin Liu, Jianhua Ma, Yi Zhang, Yang Chen, Jian Yang, et al.. Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2017, 36 (12), pp.2499-2509. ⟨10.1109/TMI.2017.2739841⟩. ⟨hal-01685727⟩

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