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  • [Med Phys.] 금속 유도 빔 강화 보정에 대한 CT 사인그램 일관성 학습 CT sinogram-consistency learning for metal-induced beam hardening correction.

    연세대 / 박형석, 서진근*

  • 출처
    Med Phys.
  • 등재일
    2018 Dec
  • 저널이슈번호
    45(12):5376-5384. doi: 10.1002/mp.13199. Epub 2018 Nov 8.
  • 내용

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    Abstract
    PURPOSE:
    This paper proposes a sinogram-consistency learning method to deal with beam hardening-related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform METHODS: The proposed learning method aims to repair inconsistent sinogram by removing the primary metal-induced beam hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data and a patient's implant type-specific learning model is used to simplify the learning process.

    RESULTS:
    The feasibility of the proposed method is investigated using a dataset, consisting of real CT scans of pelvises containing simulated hip prostheses. The anatomical areas in training and test data are different, in order to demonstrate that the proposed method extracts the beam hardening features, selectively. The results show that our method successfully corrects sinogram inconsistency by extracting beam hardening sources by means of deep learning.

    CONCLUSION:
    This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time. Conventional methods for beam hardening reduction are based on regularizations, and have the fundamental drawback of being not easily able to use manifold CT images, while a deep learning approach has the potential to do so.

     


    Author information

    Park HS1, Lee SM2, Kim HP2, Seo JK2, Chung YE3.
    1
    Division of Integrated Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Korea.
    2
    Department of Computational Science and Engineering, Yonsei University, Seoul, 120-749, Korea.
    3
    Department of Radiology, Yonsei University College of Medicine, Seoul, 03722, Korea.

  • 키워드
    computerized tomography; deep learning; metal artifact reduction; tomographic image reconstruction
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