CT sinogram-consistency learning for metal-induced beam hardening correction.
Journal:
Medical physics
Published Date:
Nov 8, 2018
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.