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dc.contributor.authorBurgos, N
dc.contributor.authorGuerreiro, F
dc.contributor.authorMcClelland, J
dc.contributor.authorPresles, B
dc.contributor.authorModat, M
dc.contributor.authorNill, S
dc.contributor.authorDearnaley, D
dc.contributor.authordeSouza, N
dc.contributor.authorOelfke, U
dc.contributor.authorKnopf, A-C
dc.contributor.authorOurselin, S
dc.contributor.authorJorge Cardoso, M
dc.date.accessioned2017-04-03T10:06:30Z
dc.date.issued2017-06
dc.identifier.citationPhysics in medicine and biology, 2017, 62 (11), pp. 4237 - 4253
dc.identifier.issn0031-9155
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/548
dc.identifier.eissn1361-6560
dc.identifier.doi10.1088/1361-6560/aa66bf
dc.description.abstractTo tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average [Formula: see text] HU and the ME [Formula: see text] HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of [Formula: see text] in the PTV for [Formula: see text], and between [Formula: see text] and 0.05% in the PTV, bladder, rectum and femur heads for D mean and [Formula: see text]. Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.
dc.formatPrint-Electronic
dc.format.extent4237 - 4253
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectJoints
dc.subjectTomography, X-Ray Computed
dc.subjectMagnetic Resonance Imaging
dc.subjectRadiotherapy Planning, Computer-Assisted
dc.subjectRadiometry
dc.subjectImage Processing, Computer-Assisted
dc.titleIterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.
dc.typeJournal Article
dcterms.dateAccepted2017-03-14
rioxxterms.versionofrecord10.1088/1361-6560/aa66bf
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2017-06
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfPhysics in medicine and biology
pubs.issue11
pubs.notesNo embargo
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/Closed research teams
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams/Clinical Academic Radiotherapy (Dearnaley)
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.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/Closed research teams
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams/Clinical Academic Radiotherapy (Dearnaley)
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-statusPublished
pubs.volume62
pubs.embargo.termsNo embargo
icr.researchteamClinical Academic Radiotherapy (Dearnaley)en_US
icr.researchteamMagnetic Resonanceen_US
icr.researchteamRadiotherapy Physics Modellingen_US
dc.contributor.icrauthorNill, Simeonen
dc.contributor.icrauthorDearnaley, Daviden
dc.contributor.icrauthorOelfke, Uween
dc.contributor.icrauthordeSouza, Nanditaen


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