  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. The proposed learning method
aims to repair inconsistent sinograms 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-type specific learning model is used to simplify the learning
process. The feasibility of the proposed method is investigated using a
dataset, consisting of real CT scan of pelvises containing 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.
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.
