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Robust phase retrieval for high resolution edge illumination x-ray phase-contrast computed tomography in non-ideal environments
Edge illumination x-ray phase contrast tomography is a recently developed imaging technique which enables three-dimensional visualisation of low-absorbing materials. Dedicated phase retrieval algorithms can provide separate computed tomography (CT) maps of sample absorption, refraction and scatterin...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977490/ https://www.ncbi.nlm.nih.gov/pubmed/27502296 http://dx.doi.org/10.1038/srep31197 |
Sumario: | Edge illumination x-ray phase contrast tomography is a recently developed imaging technique which enables three-dimensional visualisation of low-absorbing materials. Dedicated phase retrieval algorithms can provide separate computed tomography (CT) maps of sample absorption, refraction and scattering properties. In this paper we propose a novel “modified local retrieval” method which is capable of accurately retrieving sample properties in a range of realistic, non-ideal imaging environments. These include system misalignment, defects in the used optical elements and system geometry variations over time due to vibrations or temperature fluctuations. System instabilities were analysed, modelled and incorporated into a simulation study. As a result, an additional modification was introduced to the retrieval procedure to account for changes in the imaging system over time, as well as local variations over the field of view. The performance of the proposed method was evaluated in comparison to a previously used “global retrieval” method by applying both approaches to experimental CT data of a rat’s heart acquired in a non-ideal environment. The use of the proposed method resulted in the removal of major artefacts, leading to a significant improvement in image quality. This method will therefore enable acquiring high-resolution, reliable CT data of large samples in realistic settings. |
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