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dc.contributor.authorHodneland, Erlend
dc.contributor.authorHanson, Erik Andreas
dc.contributor.authorMunthe-Kaas, Antonella Z.
dc.contributor.authorLundervold, Arvid
dc.contributor.authorNordbotten, Jan Martin
dc.date.accessioned2017-03-15T14:25:07Z
dc.date.available2017-03-15T14:25:07Z
dc.date.issued2016-10
dc.identifier.citationIEEE Transactions on Biomedical Engineering 2016, 63(10):2200-2210eng
dc.identifier.urihttp://hdl.handle.net/1956/15592
dc.description.abstractObjective: Medical image registration can be formulated as a tissue deformation problem, where parameter estimation methods are used to obtain the inverse deformation. However, there is limited knowledge about the ability to recover an unknown deformation. The main objective of this study is to estimate the quality of a restored deformation field obtained from image registration of dynamic MR sequences. Methods: We investigate the behavior of forward deformation models of various complexities. Further, we study the accuracy of restored inverse deformations generated by image registration. Results: We found that the choice of 1) heterogeneous tissue parameters and 2) a poroelastic (instead of elastic) model had significant impact on the forward deformation. In the image registration problem, both 1) and 2) were found not to be significant. Here, the presence of image features were dominating the performance. We also found that existing algorithms will align images with high precision while at the same time obtain a deformation field with a relative error of 40%. Conclusion: Image registration can only moderately well restore the true deformation field. Still, estimation of volume changes instead of deformation fields can be fairly accurate and may represent a proxy for variations in tissue characteristics. Volume changes remain essentially unchanged under choice of discretization and the prevalence of pronounced image features. Significance: We suggest that image registration of high-contrast MR images has potential to be used as a tool to produce imaging biomarkers sensitive to pathology affecting tissue stiffness.eng
dc.language.isoengeng
dc.publisherIEEEeng
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/eng
dc.subjectBiot equationseng
dc.subjectdynamic imagingeng
dc.subjectelasticityeng
dc.subjectheterogeneityeng
dc.subjectimage registrationeng
dc.titlePhysical models for simulation and reconstruction of human tissue deformation fields in dynamic MRIeng
dc.typeJournal articleeng
dc.date.updated2016-12-13T10:46:28Z
dc.rights.holderCopyright 2016 The Author(s)eng
dc.type.versionpublishedVersioneng
bora.peerreviewedPeer reviewedeng
dc.type.documentJournal article
dc.identifier.cristinID1394670
dc.identifier.doi10.1109/TBME.2015.2514262eng
dc.source.issn0018-9294eng
bora.bpoaIDbpoa674


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Attribution CC BY
Except where otherwise noted, this item's license is described as Attribution CC BY