• Login
    View Item 
    •   Home
    • The Manchester Institute Cancer Research UK
    • All Paterson Institute for Cancer Research
    • View Item
    •   Home
    • The Manchester Institute Cancer Research UK
    • All Paterson Institute for Cancer Research
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of ChristieCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsProfilesView

    My Account

    LoginRegister

    Local Links

    The Christie WebsiteChristie Library and Knowledge Service

    Statistics

    Display statistics

    Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Buonaccorsi, Giovanni A
    O'Connor, James P B
    Caunce, Angela
    Roberts, Caleb
    Cheung, Susan
    Watson, Yvonne
    Davies, Karen
    Hope, Lynn
    Jackson, Alan
    Jayson, Gordon C
    Parker, Geoff J M
    Show allShow less
    Affiliation
    Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK. giob@manchester.ac.uk
    Issue Date
    2007-11
    
    Metadata
    Show full item record
    Abstract
    Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption.
    Citation
    Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data. 2007, 58 (5):1010-9 Magn Reson Med
    Journal
    Magnetic Resonance in Medicine
    URI
    http://hdl.handle.net/10541/70438
    DOI
    10.1002/mrm.21405
    PubMed ID
    17969122
    Type
    Article
    Language
    en
    ISSN
    0740-3194
    ae974a485f413a2113503eed53cd6c53
    10.1002/mrm.21405
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

    entitlement

    Related articles

    • Comparison of the performance of tracer kinetic model-driven registration for dynamic contrast enhanced MRI using different models of contrast enhancement.
    • Authors: Buonaccorsi GA, Roberts C, Cheung S, Watson Y, O'Connor JP, Davies K, Jackson A, Jayson GC, Parker GJ
    • Issue date: 2006 Sep
    • Tracer kinetic model-driven registration for dynamic contrast enhanced MRI time series.
    • Authors: Buonaccorsi GA, Roberts C, Cheung S, Watson Y, Davies K, Jackson A, Jayson GC, Parker GJ
    • Issue date: 2005
    • Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function.
    • Authors: Coll-Font J, Afacan O, Chow JS, Lee RS, Warfield SK, Kurugol S
    • Issue date: 2021 Jan
    • Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI.
    • Authors: Ariyurek C, Koçanaoğulları A, Afacan O, Kurugol S
    • Issue date: 2024 Jun
    • Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques.
    • Authors: Rajaraman S, Rodriguez JJ, Graff C, Altbach MI, Dragovich T, Sirlin CB, Korn RL, Raghunand N
    • Issue date: 2011 Jun
    DSpace software (copyright © 2002 - 2025)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.