Optimizing Data Collection for Micro X-ray Tomography
THE MICROSCOPE
2014, Volume 62:1, pp. 33–39
DOI
https://doi.org/10.59082/LXOI1902
AUTHORS
Philip W. Urnezis and Robert Myers
ABSTRACT
The use of X-ray tomography to study food dates back to the 1990s. Fruits, such as apples and peaches, were studied to determine water content and internal changes with time (1, 2). The fat deposition in pork and measurements of breast meat in broiler chickens have been investigated with X-ray tomography (3, 4). The initial work used medical CT scanners. As equipment was refined for non-medical uses, the focus of the X-ray tomography studies moved from macro features to micro features. Ice crystals that formed during freezing have been studied (5), and a cake’s microstructure was correlated to textural properties (6).
The key feature to X-ray tomography is that it is a technique that allows the interiors of an object to be three-dimensionally imaged based on density or atomic number differences. If the sample can fit within the imaging area, the sample can be imaged without having to physically alter the object, i.e. it is non-destructive. In order to achieve three-dimensional images, multiple two dimensional X-ray images are taken of the object and then are computer reconstructed into a three-dimensional depiction. The image quality determines the ability to visualize individual domains within the sample and the ability to quantify these domains. This article will discuss optimizing data collection parameters to improve the resolution for differentiation of domains and three-dimensional image quality.
The key feature to X-ray tomography is that it is a technique that allows the interiors of an object to be three-dimensionally imaged based on density or atomic number differences. If the sample can fit within the imaging area, the sample can be imaged without having to physically alter the object, i.e. it is non-destructive. In order to achieve three-dimensional images, multiple two dimensional X-ray images are taken of the object and then are computer reconstructed into a three-dimensional depiction. The image quality determines the ability to visualize individual domains within the sample and the ability to quantify these domains. This article will discuss optimizing data collection parameters to improve the resolution for differentiation of domains and three-dimensional image quality.