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17 October 2022 Deep scatter estimation for coarse anti-scatter grids as used in photon-counting CT
Julien Erath, Jan Magonov, Joscha Maier, Eric Fournié, Martin Petersilka, Karl Stierstorfer, Marc Kachelrieß
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123040J (2022) https://doi.org/10.1117/12.2646580
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Due to the smaller detector pixels in photon-counting CT, coarse anti-scatter grids are used. This may lead to high frequencies in the scattered radiation and therefore moiré artifacts in the reconstructed images can occur. It has been shown that deep convolutional neural networks are very effective to correct scatter artifacts in clinical CT. In this work we present an adapted version of the deep scatter estimation (DSE) to correct for the high frequency artifacts effectively. With the use of DSE the mean absolute error of the scatter artifacts is reduced from about 8 HU to under 1 HU. At the same time the moiré artifacts can be prevented and additional post-processing in the image can be avoided.
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Julien Erath, Jan Magonov, Joscha Maier, Eric Fournié, Martin Petersilka, Karl Stierstorfer, and Marc Kachelrieß "Deep scatter estimation for coarse anti-scatter grids as used in photon-counting CT", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123040J (17 October 2022); https://doi.org/10.1117/12.2646580
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KEYWORDS
Computed tomography
Monte Carlo methods
Neural networks
Reconstruction algorithms
Photon counting
Scattering compensation
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