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dc.contributor.authorNair, Malavika
dc.contributor.authorShepherd, Jennifer
dc.contributor.authorBest, Serena
dc.contributor.authorCameron, Ruth
dc.date.accessioned2020-03-31T23:30:39Z
dc.date.available2020-03-31T23:30:39Z
dc.date.issued2020-04-29
dc.identifier.issn1742-5689
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/303964
dc.description.abstractMicro-computed X-Ray Tomography (MicroCT) is one of the most powerful techniques available for the three dimensional characterisation of complex multi-phase or porous microarchitectures. The imaging and analysis of porous networks are of particular interest in tissue engineering due to the ability to predict various large-scale cellular phenomena through the micro-scale characterisation of the structure. However, optimising the parameters for MicroCT data capture and analyses requires a careful balance of feature resolution and computational constraints whilst ensuring that a structurally representative section is imaged and analysed. In this work, artificial datasets were used to evaluate the validity of current analytical methods by considering the effect of noise and pixel size arising from the data capture, and intrinsic structural anisotropy and heterogeneity. A novel ‘segmented percolation method’ was developed to exclude the effect of anomalous, non-representative features within the datasets, allowing for scale-invariant structural parameters to be obtained consistently and without manual intervention for the first time. Finally, an in-depth assessment of the imaging and analytical procedures are presented by considering percolation events such as micro-particle filtration and cell sieving within the context of tissue engineering. Along with the novel guidelines established for general pixel size selection for MicroCT, we also report our determination of 3 µm as the definitive pixel size for use in analysing connectivity for tissue engineering applications.
dc.description.sponsorshipMN was sponsored by Geitslich Pharma AG and the Gates Cambridge Trust
dc.publisherThe Royal Society
dc.rightsAttribution 4.0 International (CC BY)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleMicroCT analysis of connectivity in porous structures: optimising data acquisition and analytical methods in the context of tissue engineering
dc.typeArticle
prism.publicationDate2020
prism.publicationNameJournal of the Royal Society Interface
dc.identifier.doi10.17863/CAM.51048
dcterms.dateAccepted2020-03-30
rioxxterms.versionofrecord10.1098/rsif.2019.0833
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.licenseref.startdate2020-04-29
dc.contributor.orcidNair, Malavika [0000-0002-5229-8991]
dc.contributor.orcidBest, Serena [0000-0001-7866-8607]
dc.contributor.orcidCameron, Ruth [0000-0003-1573-4923]
dc.identifier.eissn1742-5662
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Research Council (320598)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N019938/1)
cam.issuedOnline2020-04-22
datacite.issupplementedby.doi10.17863/CAM.45740
cam.orpheus.successWed May 13 08:58:48 BST 2020 - Embargo updated
cam.orpheus.counter4
datacite.isderivedfrom.doi10.17863/CAM.45740
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International (CC BY)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY)