Abstract
Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes and locations, incorporating scattered radiation simulation, and including Poisson noise, the pipeline enables the expansion of limited datasets for training deep neural networks. We validate the proposed pipeline by a qualitative and quantitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.
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El-Zein, B. et al. (2024). A Realistic Collimated X-Ray Image Simulation Pipeline. In: Xue, Y., Chen, C., Chen, C., Zuo, L., Liu, Y. (eds) Data Augmentation, Labelling, and Imperfections. MICCAI 2023. Lecture Notes in Computer Science, vol 14379. Springer, Cham. https://doi.org/10.1007/978-3-031-58171-7_14
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DOI: https://doi.org/10.1007/978-3-031-58171-7_14
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