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Ring artifact reduction in synchrotron X-ray tomography through helical acquisition

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In synchrotron x-ray tomography, systematic defects in certain detector elements can result in arc-shaped artifacts in the final reconstructed image of the scanned sample. These ring artifacts are commonly found… Click to show full abstract

In synchrotron x-ray tomography, systematic defects in certain detector elements can result in arc-shaped artifacts in the final reconstructed image of the scanned sample. These ring artifacts are commonly found in many applications of synchrotron tomography, and can make it difficult or impossible to use the reconstructed image in further analyses. The severity of ring artifacts is often reduced in practice by applying pre-processing on the acquired data, or post-processing on the reconstructed image. However, such additional processing steps can introduce additional artifacts as well, and rely on specific choices of hyperparameter values. In this paper, a different approach to reducing the severity of ring artifacts is introduced: a helical acquisition mode. By moving the sample parallel to the rotation axis during the experiment, the sample is detected at different detector positions in each projection, reducing the effect of systematic errors in detector elements. Alternatively, helical acquisition can be viewed as a way to transform ring artifacts to helix-like artifacts in the reconstructed volume, reducing their severity. We show that data acquired with the proposed mode can be transformed to data acquired with a virtual circular trajectory, enabling further processing of the data with existing software packages for circular data. Results for both simulated data and experimental data show that the proposed method is able to significantly reduce ring artifacts in practice, even compared with popular existing methods, without introducing additional artifacts.

Keywords: ring artifacts; helical acquisition; synchrotron ray; ray tomography

Journal Title: Measurement Science and Technology
Year Published: 2018

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