Scientists have demonstrated the use of artificial intelligence (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.

The data captured by those detectors has all the information needed to reconstruct high-fidelity images and computational scientists can do this with advanced algorithms. These images can then help scientists design better batteries, build more durable materials, and develop better medications and treatments for diseases.

The process of using computers to assemble images from coherent scattered X-ray data is called ptychography and the team used a neural network that learns how to pull that data into a coherent form — hence the name of the innovation: PtychoNN.

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 scientists to turn that data into information that can be used. 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 one. Because the detectors can only detect amplitude and they cannot detect the phase, all that information is lost so it must be reconstructed.

It can be done but the process is slower than scientists would like. Part of the challenge is on the data acquisition end. In order to reconstruct the phase data from coherent diffraction imaging experiments, the current algorithms require scientists to collect much more amplitude data from their sample, which takes longer. But the actual reconstruction from that data takes some time as well. This is where PtychoNN comes in. Using AI techniques, the researchers 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.

For more information, contact Mathew Cherukara at This email address is being protected from spambots. You need JavaScript enabled to view it.; 630-252-4548.