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Title: New laboratory edge illumination X-ray phase contrast imaging set-ups
Author: Kallon, G. K. N.
ISNI:       0000 0004 8499 0389
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Edge illumination (EI) is an X-ray phase contrast imaging technique which has demonstrated its potential for real-world translation. The set-up consists of two periodic masks, which are aligned with each other along the beam axis. The masks are then offset to make the set-up sensitive to X-ray refraction. Despite its promise, some of EI's weaknesses must be addressed before it can be further adapted to real-world applications. For example, mask alignment conditions can be further relaxed by using a much more simplified set-up. Additionally, the current set-up must be made sensitive to refraction along two directions simultaneously, in order to match the sensitivity of the current attenuation-based X-ray imaging methods. This thesis describes the most recent developments to the EI method which aim to tackle the aforementioned issues. To achieve this, three separate experiments have been performed. The first was a study of the factors affecting EI refraction sensitivity. A flexible phase retrieval algorithm was first introduced, and was shown to produce quantitatively accurate results. This was also used to study the system's sensitivity under various conditions. Next, a single-mask EI set-up was built in the laboratory and was modelled using a wave optics computer simulation. This was then used to study the sensitivity of the single-mask method and extrapolate its value for different set-ups, while benchmarking it against the "standard" double-mask EI set-up. Finally, a two-directionally sensitive set-up is implemented in the laboratory using a novel mask design. A separate Monte Carlo simulation was used to study the alignment of the set-up, and a new, "two-directional" phase retrieval algorithm was developed. In the future, these developments can be combined into a single optimised EI set-up, which shares the cumulative benefits from all the aspects of the research undertaken here.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available