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dc.contributor.authorBlackledge, MD
dc.contributor.authorTunariu, N
dc.contributor.authorZungi, F
dc.contributor.authorHolbrey, R
dc.contributor.authorOrton, MR
dc.contributor.authorRibeiro, A
dc.contributor.authorHughes, JC
dc.contributor.authorScurr, ED
dc.contributor.authorCollins, DJ
dc.contributor.authorLeach, MO
dc.contributor.authorKoh, D-M
dc.date.accessioned2020-06-02T08:28:39Z
dc.date.issued2020-01
dc.identifier.citationFrontiers in oncology, 2020, 10 pp. 704 - ?
dc.identifier.issn2234-943X
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3663
dc.identifier.eissn2234-943X
dc.identifier.doi10.3389/fonc.2020.00704
dc.description.abstractPurpose: To characterize the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI). Methods: Three approaches to the joint modeling of voxel-wise ADC and ADC uncertainty (σADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/σADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We present the clinical feasibility of niceDWI in a pilot cohort of 16 patients with metastatic prostate cancer. Results: The statistical properties of ADC and σADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI. Conclusions: Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardize WBDWI across different scanners, by providing quantitative image signals that improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation.
dc.formatElectronic-eCollection
dc.format.extent704 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleNoise-Corrected, Exponentially Weighted, Diffusion-Weighted MRI (niceDWI) Improves Image Signal Uniformity in Whole-Body Imaging of Metastatic Prostate Cancer.
dc.typeJournal Article
dcterms.dateAccepted2020-04-15
rioxxterms.versionofrecord10.3389/fonc.2020.00704
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-01
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfFrontiers in oncology
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/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.volume10
pubs.embargo.termsNo embargo
icr.researchteamComputational Imagingen_US
dc.contributor.icrauthorBlackledge, Matthewen
dc.contributor.icrauthorKoh, Dow-Muen


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