From Event: SPIE Optical Engineering + Applications, 2017
Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common
approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation
scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and
normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain
in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be
a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution
to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to
medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the
state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT
simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to
artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our
results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate
tumor volume estimation for radiation therapy planning.
Lars Gjesteby, Qingsong Yang, Yan Xi, Hongming Shan, Bernhard Claus, Yannan Jin, Bruno De Man, and Ge Wang, "Deep learning methods for CT image-domain metal artifact reduction," Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910W (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 25 September 2017); https://doi.org/10.1117/12.2274427.
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