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dc.contributor.authorZhang, Xen
dc.contributor.authorPeng, Jen
dc.contributor.authorWang, Cen
dc.contributor.authorFeng, Yen
dc.contributor.authorFeng, Qen
dc.contributor.authorLi, Xen
dc.contributor.authorChen, Wen
dc.contributor.authorHe, Ten
dc.date.accessioned2017-09-06T09:23:50Z
dc.date.available2017-09-06T09:23:50Z
dc.date.issued2017-07-21en
dc.identifier.urihttp://hdl.handle.net/10026.1/9898
dc.description.abstract

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron.

en
dc.format.extent6158 - ?en
dc.languageengen
dc.language.isoengen
dc.subjectAlgorithmsen
dc.subjectComputer Simulationen
dc.subjectHumansen
dc.subjectImage Enhancementen
dc.subjectImage Interpretation, Computer-Assisteden
dc.subjectIronen
dc.subjectLiveren
dc.subjectPhantoms, Imagingen
dc.subjectSignal-To-Noise Ratioen
dc.subjectbeta-Thalassemiaen
dc.titleImproved Liver R2* Mapping by Averaging Decay Curves.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/28733666en
plymouth.issue1en
plymouth.volume7en
plymouth.publication-statusPublished onlineen
plymouth.journalSci Repen
dc.identifier.doi10.1038/s41598-017-05683-5en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA01 Clinical Medicine
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA01 Clinical Medicine/UoA01 Clinical Medicine
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)/CBBB
dc.publisher.placeEnglanden
dcterms.dateAccepted2017-06-01en
dc.identifier.eissn2045-2322en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1038/s41598-017-05683-5en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-07-21en
rioxxterms.typeJournal Article/Reviewen


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