Open AccessHigh cone-angle x-ray computed micro-tomography with 186 GigaVoxel datasets
Author(s) -
Glenn R. Myers,
Shane Latham,
Andrew Kingston,
Jan Kolomazník,
Václav Krajíček,
Tomáš Krupka,
Trond Varslot,
Adrian Sheppard
Publication year - 2016
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.2238258
Subject(s) - python (programming language) , voxel , software , cuda , high resolution , tomography , computed tomography , computer science , resolution (logic) , image resolution , computer graphics (images) , computational science , materials science , optics , artificial intelligence , physics , geology , parallel computing , remote sensing , medicine , radiology , programming language , operating system
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.
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