Reprojection Alignment for Trajectory Perturbation Estimation in Microtomography

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dc.contributor.authorLatham, Shane
dc.contributor.authorKingston, Andrew
dc.contributor.authorRecur, Benoit
dc.contributor.authorMyers, Glenn
dc.contributor.authorDelgado-Friedrichs, Olaf
dc.contributor.authorSheppard, Adrian
dc.date.accessioned2019-04-20T07:56:54Z
dc.date.issued2018
dc.date.updated2019-03-12T07:32:01Z
dc.description.abstractFor standard laboratory microtomography systems, acquired radiographs do not always adhere to the strict geometrical assumptions of the reconstruction algorithm. The consequence of this geometrical inconsistency is that the reconstructed tomogram contains motion artifacts, e.g., blurring, streaking, double-edges. To achieve a motion-artifact-free tomographic reconstruction, one must estimate, and subsequently correct for, the per-radiograph experimental geometry parameters. In this paper, we examine the use of re-projection alignment (RA) to estimate per-radiograph geometry. Our simulations evaluate how the convergence properties of RA vary with: motion-type (smooth versus random), trajectory (helical versus discrete-sampling `space-filling' trajectories) and tomogram resolution. The idealized simulations demonstrate for the space-filling trajectory that RA convergence rate and accuracy is invariant with regard to the motion-type and that the per-projection motions can be estimated to less than 0.25 pixel mean absolute error by performing a single quarter-resolution RA iteration followed by a single half-resolution RA iteration. The direct impact is that, for the space-filling trajectory, one can incorporate RA in an iterative multi-grid reconstruction scheme with only a single RA iteration per multi-grid resolution step. We also find that for either trajectory, slowly varying vertical errors cannot be reliably estimated by employing the RA method alone; such errors are indistinguishable from a trajectory of different pitch. This has minimal effect in practice because RA can be combined with reference frame correction which is effective for correcting low-frequency errors.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2333-9403en_AU
dc.identifier.urihttp://hdl.handle.net/1885/160505
dc.language.isoen_AUen_AU
dc.publisherIEEEen_AU
dc.sourceIEEE Transactions on Computational Imagingen_AU
dc.titleReprojection Alignment for Trajectory Perturbation Estimation in Microtomographyen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue2en_AU
local.contributor.affiliationLatham, Shane, College of Science, ANUen_AU
local.contributor.affiliationKingston, Andrew, College of Science, ANUen_AU
local.contributor.affiliationRecur, Benoit, College of Science, ANUen_AU
local.contributor.affiliationMyers, Glenn, College of Science, ANUen_AU
local.contributor.affiliationDelgado-Friedrichs, Olaf, College of Science, ANUen_AU
local.contributor.affiliationSheppard, Adrian, College of Science, ANUen_AU
local.contributor.authoremailu3813363@anu.edu.auen_AU
local.contributor.authoruidLatham, Shane, u3813363en_AU
local.contributor.authoruidKingston, Andrew, u4438507en_AU
local.contributor.authoruidRecur, Benoit, u5450832en_AU
local.contributor.authoruidMyers, Glenn, u4703841en_AU
local.contributor.authoruidDelgado-Friedrichs, Olaf, u4452761en_AU
local.contributor.authoruidSheppard, Adrian, u9204025en_AU
local.description.embargo2040-01-01
local.description.notesImported from ARIESen_AU
local.identifier.absfor080110 - Simulation and Modellingen_AU
local.identifier.absseo890205 - Information Processing Services (incl. Data Entry and Capture)en_AU
local.identifier.ariespublicationu4485658xPUB1986en_AU
local.identifier.citationvolume4en_AU
local.identifier.doi10.1109/TCI.2018.2811945en_AU
local.identifier.thomsonID000431974300008
local.identifier.uidSubmittedByu4485658en_AU
local.type.statusPublished Versionen_AU

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