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CoWork webinar series

CDI principles and algorithms

Time: Thu 2020-10-22 14.00 - 15.00

Location: Online

Lecturer: Dr. Tomas Ekeberg

The CoWork webinar series is dedicated to the exploitation of the coherence properties of X-rays for advanced materials characterization, with a special focus on inverse microscopy techniques, such as Coherent Diffraction Imaging (CDI), Ptychography and Holography. It is an introduction to Coherent X-ray imaging methods to facilitate the access to advanced microscopy techniques to new users and it welcomes all researchers intrigued by the spectacular coherence properties of X-rays produced at modern synchrotron sources – of which MAX IV is a first example.

When: Thursday Oct 22, 14.00 - 15.00
Speaker: Dr. Tomas Ekeberg, Uppsala University, Sweden
Title: CDI principles and algorithms
Zoom link/registration (Register in advance for this meeting):
After registering, you will receive a confirmation email containing information about joining the meeting.

Dr. Tomas Ekeberg is a researcher in the laboratory of Molecular Biophysics at Uppsala University. His work is focused on developing new methods for structural studies of viruses and macromolecules enabled by the ultrafast X-ray pulses from free-electron lasers. He combines simulations and experimental data to investigate and improve the data-analysis algorithms involved to go from raw diffraction to biological structure.

X-rays are excellent probes for studying many different types of matter ranging from biological macromolecules to porous rocks at resolutions far beyond what is possible using optical wavelengths. However, when it comes to imaging, X-rays have one glaring weakness: it’s very hard to make a good X-ray lens and the ones that exist are limited in both resolution and efficiency. So, if you want to study a very small sample your image will be both blurry and weak. In Coherent diffraction imaging (CDI), the lens is therefore usually replaced with a detector and we use computer algorithms to retrieve the electron density of the sample from the measured diffraction intensities.

Solving this inverse problem introduces an extra computational step, which under the wrong circumstances can be both hard and introduce artefacts or even be outright impossible. But when it’s successful it means that we can use the full potential of X-rays as a probe, limited only by the strength of our X-rays or the damage that it induces in the sample.

In this webinar I will explain how the electron density relates to its diffraction patterns, and how this knowledge can help us to solve the inversion problem. You will see several inversion techniques and I will highlight some of the innovations that make today’s algorithms successful, but I will also show you the cases where they are not. Things get even more interesting when several diffraction patterns from the same or identical samples are combined into a 3D diffraction dataset and the same inversion methods can be used to study particles in full 3D.

Last, I will show you examples from my own work, where we use the X-rays from a free-electron laser to study proteins in motion. The ultra-short pulses mean that we can capture diffraction data before the time it takes for the sample to degrade due to the pulse and therefore also radiation damage will no longer be a limiting factor. This allows us to study small isolated particles such as proteins and viruses. If we can sort the diffraction patterns according to the state of motion that each particle was in we can now retrieve information not only about its structure but also about dynamics and function.

Belongs to: Center for X-rays in Swedish Materials Science (CeXS)
Last changed: Nov 10, 2020