Comparison of the performance of tracer kinetic model-driven registration for dynamic contrast enhanced MRI using different models of contrast enhancement.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
Buonaccorsi
Giovanni A
GA
Department of Imaging Science and Biomedical Engineering, Stopford Building, Oxford Road, University of Manchester, and Cancer Research UK Department of Medical Oncology, Christie Hospital NHS Trust, UK. giob@manchester.ac.uk
Roberts
Caleb
C
Cheung
Sue
S
Watson
Yvonne
Y
O'Connor
James P B
JP
Davies
Karen
K
Jackson
Alan
A
Jayson
Gordon C
GC
Parker
Geoff J M
GJ
D000008:Abdominal Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media