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dc.contributor.authorTunariu, N
dc.contributor.authorBlackledge, M
dc.contributor.authorMessiou, C
dc.contributor.authorPetralia, G
dc.contributor.authorPadhani, A
dc.contributor.authorCurcean, S
dc.contributor.authorCurcean, A
dc.contributor.authorKoh, D-M
dc.date.accessioned2021-02-11T12:07:39Z
dc.date.available2021-02-11T12:07:39Z
dc.identifier.citationThe British journal of radiology, 2020, 93 (1115), pp. 20200562 - ?
dc.identifier.issn0007-1285
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4345
dc.identifier.eissn1748-880X
dc.identifier.doi10.1259/bjr.20200562
dc.description.abstractWhole-body MRI (WB-MRI) has evolved since its first introduction in the 1970s as an imaging technique to detect and survey disease across multiple sites and organ systems in the body. The development of diffusion-weighted MRI (DWI) has added a new dimension to the implementation of WB-MRI on modern scanners, offering excellent lesion-to-background contrast, while achieving acceptable spatial resolution to detect focal lesions 5 to 10 mm in size. MRI hardware and software advances have reduced acquisition times, with studies taking 40-50 min to complete.The rising awareness of medical radiation exposure coupled with the advantages of MRI has resulted in increased utilization of WB-MRI in oncology, paediatrics, rheumatological and musculoskeletal conditions and more recently in population screening. There is recognition that WB-MRI can be used to track disease evolution and monitor response heterogeneity in patients with cancer. There are also opportunities to combine WB-MRI with molecular imaging on PET-MRI systems to harness the strengths of hybrid imaging. The advent of artificial intelligence and machine learning will shorten image acquisition times and image analyses, making the technique more competitive against other imaging technologies.
dc.formatPrint-Electronic
dc.format.extent20200562 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectGenetic Predisposition to Disease
dc.subjectPositron-Emission Tomography
dc.subjectMagnetic Resonance Imaging
dc.subjectDiffusion Magnetic Resonance Imaging
dc.subjectIncidental Findings
dc.subjectTime Factors
dc.subjectHistory, 21st Century
dc.subjectFemale
dc.subjectMale
dc.subjectWhole Body Imaging
dc.subjectMultimodal Imaging
dc.subjectRadiation Exposure
dc.titleWhat's New for Clinical Whole-body MRI (WB-MRI) in the 21st Century.
dc.typeJournal Article
dcterms.dateAccepted2020-08-07
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1259/bjr.20200562
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfThe British journal of radiology
pubs.issue1115
pubs.notesNot known
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/Computational Imaging
pubs.organisational-group/ICR/Primary Group/Royal Marsden Clinical Units
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/Computational Imaging
pubs.organisational-group/ICR/Primary Group/Royal Marsden Clinical Units
pubs.publication-statusPublished
pubs.volume93
pubs.embargo.termsNot known
icr.researchteamComputational Imaging
icr.researchteamComputational Imagingen_US
dc.contributor.icrauthorKoh, Dow-Muen
dc.contributor.icrauthorBlackledge, Matthewen
dc.contributor.icrauthorTunariu, Ninaen
dc.contributor.icrauthorMessiou, Christinaen


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