X-ray scatter is most significant when imaging very dense/large samples: e.g. metal parts, large 3D printed components, or samples imaged on the CTLab's new "whole core" scanner. The student will develop methods to correct for its effects, both in-hardware (i.e. at the microscope) and in-software (i.e. image analysis).

In this project the student will explore a cutting-edge "speckle tracking" method for measuring X-ray phase, in which computational image analysis is used to infer the X-ray phase from deformations in a known speckle pattern. This has both theoretical and experimental components.

Machine learning (and in particular deep-learning) methods have been at the centre of amazing progress in the field of computational image analysis. In this project the student will work to develop machine-learning algorithms for tomographic reconstruction, and deploy these algorithms at the ANU CTLab imaging facility.

In ghost imaging, images are formed based on photons that have never interacted with the sample. 3D ghost imaging was first performed in 2018 by scientists at ANU and international collaborators at the European Synchrotron Radiation Facility: the student will work with these scientists to further advance the field.