Myers, GlennLatham, ShaneKingston, AndrewKolomazník, JanKrajíček, VáclavKrupka, TomášVarslot, Trond K.Sheppard, Adrian2019-12-172019-12-17Glenn R. Myers, Shane J. Latham, Andrew M. Kingston, Jan Kolomazník, Václav Krajíček, Tomáš Krupka, Trond K. Varslot, 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); doi: 10.1117/12.22382580277-786Xhttp://hdl.handle.net/1885/195615X-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 difficult for these materials below 2mm diameter: consequently, a typical ultra-micro-CT reconstruction volume (with sub-micron resolution) will be around 3k × 3k × 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.This research was supported under the Australian Research Council’s Linkage Projects funding scheme (project number LP150101040), in collaboration with FEI.application/pdfen-AU© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)Micro-tomographyX-ray computed tomographyIterative tomographic reconstructionGPGPUMPIParallel computingBig data2016-10-0410.1117/12.2238258