X-ray lens figure errors retrieved by deep learning from several beam intensity images.
Manuel Sanchez Del RioRafael CelestreJuan Reyes-HerreraPublished in: Journal of synchrotron radiation (2024)
The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.
Keyphrases
- deep learning
- neural network
- high resolution
- cataract surgery
- electron microscopy
- convolutional neural network
- high intensity
- dual energy
- patient safety
- artificial intelligence
- copy number
- monte carlo
- optical coherence tomography
- molecular dynamics
- resistance training
- adverse drug
- emergency department
- gene expression
- high speed