Method for numeric estimation of Cupping effect on CT images

Anastasia Ingacheva; Marina Chukalina; Alexey Buzmakov; Dmitri Nikolaev

doi:10.1117/12.2557167

31 January 2020 Method for numeric estimation of Cupping effect on CT images

Anastasia Ingacheva, Marina Chukalina, Alexey Buzmakov, Dmitri Nikolaev

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143331 (2020) https://doi.org/10.1117/12.2557167
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Usage of common reconstruction algorithms like Filtered Back Projection and Algebraic Reconstruction Technique to the projection data acquired with poly-chromatic probing radiation leads to the appearance of a cup-like distortion of the value profile in reconstructed images. While many methods of the poly-chromatic probing artifacts suppression are suggested, the numerical estimation algorithm of the “Cupping effect” typically is not considered to be important. Described methods imply manual regions selection where the intensity will be compared, or just use experts’ opinion on the effect presence. In this paper, we suggest automatic estimation of the “Cupping effect” method based on utilizing the distance transform built using the objects mask. As a result, we obtain a numeric estimation of the intensity change from the border to the center of the object. As the final image index, a weighted sum of the ratings of all objects is used. While positive value shows the magnitude of the “Cupping effect”, a negative value, on the contrary, shows magnitude of the reverse “Cupping effect”. In the paper, we demonstrate the method used on simulated data and compare it with several different techniques for distortion evaluation due to poly-chromatic probing. Finally, we show method effectiveness on real data acquired with laboratory tomography.

Citation Download Citation

Anastasia Ingacheva, Marina Chukalina, Alexey Buzmakov, and Dmitri Nikolaev "Method for numeric estimation of Cupping effect on CT images", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143331 (31 January 2020); https://doi.org/10.1117/12.2557167

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