Investigation of Different Sparsity Transforms for the PICCS Algorithm in Small- Animal Respiratory Gated CT

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Show simple item record Abascal, Juan Abella García, Mónica Sisniega, Alejandro Vaquero López, Juan José Desco Menéndez, Manuel 2015-04-07T12:54:14Z 2015-04-07T12:54:14Z 2015-04-02
dc.identifier.bibliographicCitation Abascal JFPJ, Abella M, Sisniega A, Vaquero JJ, Desco M (2015) Investigation of Different Sparsity Transforms for the PICCS Algorithm in Small-Animal Respiratory Gated CT. PLoS ONE 10 (4): e0120140.
dc.description Data Availability Statement: All relevant data are available from the Zenodo database, under the DOI:
dc.description.abstract Respiratory gating helps to overcome the problem of breathing motion in cardiothoracic small-animal imaging by acquiring multiple images for each projection angle and then assigning projections to different phases. When this approach is used with a dose similar to that of a static acquisition, a low number of noisy projections are available for the reconstruction of each respiratory phase, thus leading to streak artifacts in the reconstructed images. This problem can be alleviated using a prior image constrained compressed sensing (PICCS) algorithm, which enables accurate reconstruction of highly undersampled data when a prior image is available. We compared variants of the PICCS algorithm with different transforms in the prior penalty function: gradient, unitary, and wavelet transform. In all cases the problem was solved using the Split Bregman approach, which is efficient for convex constrained optimization. The algorithms were evaluated using simulations generated from data previously acquired on a micro-CT scanner following a high-dose protocol (four times the dose of a standard static protocol). The resulting data were used to simulate scenarios with different dose levels and numbers of projections. All compressed sensing methods performed very similarly in terms of noise, spatiotemporal resolution, and streak reduction, and filtered back-projection was greatly improved. Nevertheless, the wavelet domain was found to be less prone to patchy cartoon-like artifacts than the commonly used gradient domain.
dc.description.sponsorship This work was partially funded by the RICRETIC network (RD12/0042/0057) from the Ministerio de Economía y Competitividad ( and projects TEC2010-21619-C04-01 and PI11/00616 from Ministerio de Ciencia e Innovación ( The research leading to these results was supported by funding from the Innovative Medicines Initiative ( Joint Undertaking under grant agreement n°115337, the resources of which comprise financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies ("in kind contribution"). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.format.extent 18
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.uri info:eu-repo/semantics/dataset/doi/10.5281/zenodo.15685
dc.rights © The autors
dc.rights Atribución-NoComercial 3.0 España
dc.title Investigation of Different Sparsity Transforms for the PICCS Algorithm in Small- Animal Respiratory Gated CT
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi 10.1371/journal.pone.0120140
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. RD12/0042/0057
dc.relation.projectID Gobierno de España. TEC2010-21619-C04-01
dc.relation.projectID Gobierno de España. PI11/00616
dc.type.version publishedVersion
dc.identifier.publicationissue 4
dc.identifier.publicationtitle PLoS ONE
dc.identifier.publicationvolume 10
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