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Iterative reconstruction optimisations for high angle cone-beam micro-CT
Author(s) -
Benoît Recur,
Matthias Fauconneau,
Andrew Kingston,
Glenn R. Myers,
Adrian Sheppard
Publication year - 2014
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 - 10.1117/12.2062450
Subject(s) - computer science , trajectory , ligand cone angle , voxel , iterative reconstruction , noise (video) , algorithm , signal (programming language) , signal to noise ratio (imaging) , cone (formal languages) , volume (thermodynamics) , beam (structure) , artificial intelligence , optics , computer vision , physics , geometry , telecommunications , mathematics , conical surface , astronomy , quantum mechanics , image (mathematics) , programming language
We address several acquisition questions that have arisen for the high cone-angle helical-scanning micro-CT facility developed at the Australian National University. These challenges are generally known in medical and industrial cone-beam scanners but can be neglected in these systems. For our large datasets, with more than 20483 voxels, minimising the number of operations (or iterations) is crucial. Large cone-angles enable high signal-to-noise ratio imaging and a large helical pitch to be used. This introduces two challenges: (i) non-uniform resolution throughout the reconstruction, (ii) over-scan beyond the region-of-interest significantly increases re- quired reconstructed volume size. Challenge (i) can be addressed by using a double-helix or lower pitch helix but both solutions slow down iterations. Challenge (ii) can also be improved by using a lower pitch helix but results in more projections slowing down iterations. This may be overcome using less projections per revolution but leads to more iterations required. Here we assume a given total time for acquisition and a given reconstruction technique (SART) and seek to identify the optimal trajectory and number of projections per revolution in order to produce the best tomogram, minimise reconstruction time required, and minimise memory requirements.

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