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.
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).
Generating 3D volumes, i.e., tomography, of an object as it changes over time (or evolves) is a challenging problem. The ability to achieve this would reveal new information and understanding of many dynamic processes.
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.
This project involves working with scientists from imaging beamlines at the Australian Synchrotron (IMBL, XFM, MCT) and the Lucas Heights nuclear reactor (DINGO) to develop multi-modal, multi-scale, and dynamic imaging and tomography techniques alongside computational imaging scientists from ANU.
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.
This project will use unique, ANU-designed 3D X-ray microscopes and state-of-the art image analysis to track physiological responses of drought-tolerant Australian plants when subjected to water stress. The results will help us understand the mechanisms that underpin drought-tolerance, helping resolve ongoing debates and helping understand which forest eco-systems that are most vulnerable to climate change, and why.
This project will involve building a unified model of several theoretically-complex X-ray behaviours within the microscopes at the ANU CTLab, drawing from statistical and wave optics: spatial partial-coherence, refraction, and spectral interactions. The student will then apply this model to improve imaging capabilities at the ANU CTLab.
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