Summary of A Realistic Collimated X-ray Image Simulation Pipeline, by Benjamin El-zein et al.
A Realistic Collimated X-Ray Image Simulation Pipeline
by Benjamin El-Zein, Dominik Eckert, Thomas Weber, Maximilian Rohleder, Ludwig Ritschl, Steffen Kappler, Andreas Maier
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel image processing pipeline simulates X-ray images with collimator shadows, addressing limitations in detector positioning information. The pipeline generates randomized labels for collimator shapes and locations, incorporates scattered radiation simulation, and adds Poisson noise. This enables the expansion of limited datasets for training deep neural networks. The approach is validated through qualitative and quantitative comparisons against real collimator shadows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary X-ray systems struggle to detect collimators when detector position information is unavailable or unreliable. A new image processing pipeline helps solve this problem by creating fake X-ray images that mimic real-world scenarios. By generating random labels for collimator shapes, simulating scattered radiation, and adding noise, the pipeline allows researchers to train deep neural networks with limited data. This approach is tested against real collimator shadows and shows promising results. |
