A Realistic Collimated X-Ray Image Simulation Pipeline
This work addresses a domain-specific problem in medical imaging by providing a tool to enhance training data for collimator detection, which is incremental as it builds on existing simulation and deep learning methods.
The paper tackles the challenge of collimator detection in X-ray systems by developing a physically motivated simulation pipeline that generates realistic collimator shadows, enabling dataset expansion for training deep neural networks, and it shows that using simulated data improves generalization on real-world data.
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.