Paper
4 October 2016 High cone-angle x-ray computed micro-tomography with 186 GigaVoxel datasets
Glenn R. Myers, Shane J. Latham, Andrew M. Kingston, Jan Kolomazník, Václav Krajíček, Tomáš Krupka, Trond K. Varslot, Adrian P. Sheppard
Author Affiliations +
Abstract
X-ray computed micro-tomography systems are able to collect data with sub-micron resolution. This high- resolution imaging has many applications but is particularly important in the study of porous materials, where the sub-micron structure can dictate large-scale physical properties (e.g. carbonates, shales, or human bone). Sample preparation and mounting become diffiult for these materials below 2mm diameter: consequently, a typical ultra-micro-CT reconstruction volume (with sub-micron resolution) will be around 3k x 3k x 10k voxels, with some reconstructions becoming much larger. In this paper, we discuss the hardware (MPI-parallel CPU/GPU) and software (python/C++/CUDA) tools used at the ANU CTlab to reconstruct ~186 GigaVoxel datasets.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Glenn R. Myers, Shane J. Latham, Andrew M. Kingston, Jan Kolomazník, Václav Krajíček, Tomáš Krupka, Trond K. Varslot, and Adrian P. Sheppard "High cone-angle x-ray computed micro-tomography with 186 GigaVoxel datasets", Proc. SPIE 9967, Developments in X-Ray Tomography X, 99670U (4 October 2016); https://doi.org/10.1117/12.2238258
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Cited by 8 scholarly publications.
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KEYWORDS
Radiography

Reconstruction algorithms

X-rays

X-ray imaging

CT reconstruction

Image resolution

X-ray computed tomography

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