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dc.contributor.authorBano, W
dc.contributor.authorHolmes, W
dc.contributor.authorGoodburn, R
dc.contributor.authorGolbabaee, M
dc.contributor.authorGupta, A
dc.contributor.authorWithey, S
dc.contributor.authorTree, A
dc.contributor.authorOelfke, U
dc.contributor.authorWetscherek, A
dc.coverage.spatialUnited States
dc.date.accessioned2023-09-06T09:37:59Z
dc.date.available2023-09-06T09:37:59Z
dc.date.issued2023-05-27
dc.identifier.citationMedical Physics, 2023,
dc.identifier.issn0094-2405
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5959
dc.identifier.eissn2473-4209
dc.identifier.eissn2473-4209
dc.identifier.doi10.1002/mp.16479
dc.description.abstractBACKGROUND: T2 * mapping can characterize tumor hypoxia, which may be associated with resistance to therapy. Acquiring T2 * maps during MR-guided radiotherapy could inform treatment adaptation by, for example, escalating the dose to resistant sub-volumes. PURPOSE: The purpose of this work is to demonstrate the feasibility of the accelerated T2 * mapping technique using model-based image reconstruction with integrated trajectory auto-correction (TrACR) for MR-guided radiotherapy on an MR-Linear accelerator (MR-Linac). MATERIALS AND METHODS: The proposed method was validated in a numerical phantom, where two T2 * mapping approaches (sequential and joint) were compared for different noise levels (0,0.1,0.5,1) and gradient delays ([1, -1] and [1, -2] in units of dwell time for x- and y-axis, respectively). Fully sampled k-space was retrospectively undersampled using two different undersampling patterns. Root mean square errors (RMSEs) were calculated between reconstructed T2 * maps and ground truth. In vivo data was acquired twice weekly in one prostate and one head and neck cancer patient undergoing treatment on a 1.5 T MR-Linac. Data were retrospectively undersampled and T2 * maps reconstructed, with and without trajectory corrections were compared. RESULTS: Numerical simulations demonstrated that, for all noise levels, T2 * maps reconstructed with a joint approach demonstrated less error compared to an uncorrected and sequential approach. For a noise level of 0.1, uniform undersampling and gradient delay [1, -1] (in units of dwell time for x- and y-axis, respectively), RMSEs for sequential and joint approaches were 13.01 and 9.32 ms, respectively, which reduced to 10.92 and 5.89 ms for a gradient delay of [1, 2]. Similarly, for alternate undersampling and gradient delay [1, -1], RMSEs for sequential and joint approaches were 9.80 and 8.90 ms, respectively, which reduced to 9.10 and 5.40 ms for gradient delay [1, 2]. For in vivo data, T2 * maps reconstructed with our proposed approach resulted in less artifacts and improved visual appearance compared to the uncorrected approach. For both prostate and head and neck cancer patients, T2 * maps reconstructed from different treatment fractions showed changes within the planning target volume (PTV). CONCLUSION: Using the proposed approach, a retrospective data-driven gradient delay correction can be performed, which is particularly relevant for hybrid devices, where full information on the machine configuration is not available for image reconstruction. T2 * maps were acquired in under 5 min and can be integrated into MR-guided radiotherapy treatment workflows, which minimizes patient burden and leaves time for additional imaging for online adaptive radiotherapy on an MR-Linac.
dc.formatPrint-Electronic
dc.languageeng
dc.language.isoeng
dc.publisherWILEY
dc.relation.ispartofMedical Physics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMR-Linac
dc.subjectT2* mapping
dc.subjectgradient delay correction
dc.subjecthypoxia imaging
dc.titleJoint radial trajectory correction for accelerated T2 * mapping on an MR-Linac.
dc.typeJournal Article
dcterms.dateAccepted2023-04-28
dc.date.updated2023-09-06T09:37:29Z
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1002/mp.16479
rioxxterms.licenseref.startdate2023-05-27
rioxxterms.typeJournal Article/Review
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37245075
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/Radiotherapy Physics Modelling
pubs.publication-statusPublished online
pubs.publisher-urlhttp://dx.doi.org/10.1002/mp.16479
icr.researchteamRadiother Phys Modelling
dc.contributor.icrauthorWetscherek, Andreas
icr.provenanceDeposited by Mr Arek Surman on 2023-09-06. Deposit type is initial. No. of files: 1. Files: Medical Physics - 2023 - Bano.pdf


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