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dc.contributor.authorFreedman, JNen_US
dc.contributor.authorCollins, DJen_US
dc.contributor.authorGurney-Champion, OJen_US
dc.contributor.authorMcClelland, JRen_US
dc.contributor.authorNill, Sen_US
dc.contributor.authorOelfke, Uen_US
dc.contributor.authorLeach, MOen_US
dc.contributor.authorWetscherek, Aen_US
dc.coverage.spatialIrelanden_US
dc.date.accessioned2018-06-06T09:15:25Z
dc.date.issued2018-12en_US
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/29871813en_US
dc.identifierS0167-8140(18)30274-3en_US
dc.identifier.citationRadiother Oncol, 2018, 129 (3), pp. 486 - 493en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1728
dc.identifier.eissn1879-0887en_US
dc.identifier.doi10.1016/j.radonc.2018.05.015en_US
dc.description.abstractBACKGROUND AND PURPOSE: The superior soft-tissue contrast of 4D-T2w MRI motivates its use for delineation in radiotherapy treatment planning. We address current limitations of slice-selective implementations, including thick slices and artefacts originating from data incompleteness and variable breathing. MATERIALS AND METHODS: A method was developed to calculate midposition and 4D-T2w images of the whole thorax from continuously acquired axial and sagittal 2D-T2w MRI (1.5 × 1.5 × 5.0 mm3). The method employed image-derived respiratory surrogates, deformable image registration and super-resolution reconstruction. Volunteer imaging and a respiratory motion phantom were used for validation. The minimum number of dynamic acquisitions needed to calculate a representative midposition image was investigated by retrospectively subsampling the data (10-30 dynamic acquisitions). RESULTS: Super-resolution 4D-T2w MRI (1.0 × 1.0 × 1.0 mm3, 8 respiratory phases) did not suffer from data incompleteness and exhibited reduced stitching artefacts compared to sorted multi-slice MRI. Experiments using a respiratory motion phantom and colour-intensity projection images demonstrated a minor underestimation of the motion range. Midposition diaphragm differences in retrospectively subsampled acquisitions were <1.1 mm compared to the full dataset. 10 dynamic acquisitions were found sufficient to generate midposition MRI. CONCLUSIONS: A motion-modelling and super-resolution method was developed to calculate high quality 4D/midposition T2w MRI from orthogonal 2D-T2w MRI.en_US
dc.format.extent486 - 493en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subject4D MRIen_US
dc.subjectMotion vector fielden_US
dc.subjectRadiotherapy treatment planningen_US
dc.subjectSuper resolutionen_US
dc.subjectT2w 4D MRIen_US
dc.titleSuper-resolution T2-weighted 4D MRI for image guided radiotherapy.en_US
dc.typeJournal Article
dcterms.dateAccepted2018-05-14en_US
rioxxterms.versionofrecord10.1016/j.radonc.2018.05.015en_US
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0en_US
rioxxterms.licenseref.startdate2018-12en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfRadiother Oncolen_US
pubs.issue3en_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Magnetic Resonance
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.publication-statusPublisheden_US
pubs.volume129en_US
pubs.embargo.termsNot knownen_US
icr.researchteamMagnetic Resonanceen_US
icr.researchteamRadiotherapy Physics Modellingen_US
dc.contributor.icrauthorFreedman, Joshuaen_US
dc.contributor.icrauthorGurney-Champion, Oliveren_US
dc.contributor.icrauthorNill, Simeonen_US
dc.contributor.icrauthorOelfke, Uween_US
dc.contributor.icrauthorLeach, Martinen_US
dc.contributor.icrauthorWetscherek, Andreasen_US


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