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Article Dans Une Revue IEEE Transactions on Medical Imaging Année : 2017

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

Jianhua Ma
  • Fonction : Auteur
Jian Yang
  • Fonction : Auteur
  • PersonId : 922120
Wei Yang
  • Fonction : Auteur
  • PersonId : 922120
Qianjin Feng
  • Fonction : Auteur
Wufan Chen
  • Fonction : Auteur
  • PersonId : 922120

Résumé

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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Dates et versions

hal-01685727 , version 1 (16-01-2018)

Identifiants

Citer

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, 2017, 36 (12), pp.2499-2509. ⟨10.1109/TMI.2017.2739841⟩. ⟨hal-01685727⟩
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