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Photon Sciences

PtychoNN Uses Neural Networks for Faster X-ray Imaging

Argonne scientists are using artificial intelligence techniques to decode X-ray images faster, which could aid innovations in medicine, materials, and energy.

A team of computer scientists from two U.S. Department of Energy (DOE) Office of Science User Facilities at DOE’s Argonne National Laboratory – the Advanced Photon Source (APS) and Center for Nanoscale Materials (CNM) – have demonstrated the use of AI to speed up the process of reconstructing images from coherent X-ray scattering data.

Traditional X-ray imaging techniques (like medical x-ray images) are limited in the amount of detail they can provide. This has led to the development of coherent x-ray imaging methods that are capable of providing images from deep within materials at a few nanometer resolution or less. These techniques generate X-ray images without the need for lenses, by diffracting or scattering the beam off of samples and directly onto detectors.

When an X-ray beam strikes a sample, the light is diffracted and scatters, and the detectors around the sample collect that light. It’s then up to computer scientists to turn that data into information scientists can use. The challenge, however, is that while the photons in the X-ray beam carry two pieces of information – the amplitude, or the brightness of the beam, and the phase, or how much the beam changes when it passes through the sample – the detectors only capture the amplitude.

This is where PtychoNN comes in. Using AI techniques, the team of researchers has demonstrated that computers can be taught to predict and reconstruct images from X-ray data, and can do it 300 times faster than the traditional method. More than that, though, PtychoNN is able to speed up the process on both ends.

This efficiency bodes well for PtychoNN as a new way to process data after the completion of the APS Upgrade. This approach will allow data analysis and image recovery to keep up with the increase in data.