Comparison of the Performance of Tracer Kinetic Model-Driven Registration for Dynamic Contrast Enhanced MRI Using Different Models of Contrast Enhancement

Giovanni Buonaccorsi, Caleb Roberts, Susan Cheung, Yvonne Watson, James P B O'Connor, Karen Davies, Alan Jackson, Gordon C. Jayson, Geoff J M Parker

    Research output: Contribution to journalArticlepeer-review


    Rationale and Objectives: The quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data is subject to model fitting errors caused by motion during the time-series data acquisition. However, the time-varying features that occur as a result of contrast enhancement can confound motion correction techniques based on conventional registration similarity measures. We have therefore developed a heuristic, locally controlled tracer kinetic model-driven registration procedure, in which the model accounts for contrast enhancement, and applied it to the registration of abdominal DCE-MRI data at high temporal resolution. Materials and Methods: Using severely motion-corrupted data sets that had been excluded from analysis in a clinical trial of an antiangiogenic agent, we compared the results obtained when using different models to drive the tracer kinetic model-driven registration with those obtained when using a conventional registration against the time series mean image volume. Results: Using tracer kinetic model-driven registration, it was possible to improve model fitting by reducing the sum of squared errors but the improvement was only realized when using a model that adequately described the features of the time series data. The registration against the time series mean significantly distorted the time series data, as did tracer kinetic model-driven registration using a simpler model of contrast enhancement. Conclusion: When an appropriate model is used, tracer kinetic model-driven registration influences motion-corrupted model fit parameter estimates and provides significant improvements in localization in three-dimensional parameter maps. This has positive implications for the use of quantitative DCE-MRI for example in clinical trials of antiangiogenic or antivascular agents. © 2006 AUR.
    Original languageEnglish
    Pages (from-to)1112-1123
    Number of pages11
    JournalAcademic Radiology
    Issue number9
    Publication statusPublished - Sept 2006


    • angiogenesis inhibitors
    • computer-assisted
    • Gd-DTPA
    • image processing
    • image registration
    • MRI


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