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

Authors:
Giovanni A. Buonaccorsi;Caleb Roberts;Sue Cheung;Yvonne Watson;Karen Davies;Alan Jackson;Gordon C. Jayson;Geoff J. M. Parker
Affiliations:
Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Cancer Research UK Dept of Medical Oncology, Christie Hospital, Manchester, UK;Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

Motion during time-series data acquisition causes model-fitting errors in quantitative dynamic contrast-enhanced (DCE) MRI studies. Motion correction techniques using conventional registration cost functions may produce biased results because they were not designed to deal with the time-varying information content due to contrast enhancement. We present a locally-controlled, 3D translational registration process driven by tracer kinetic modeling that successfully registers abdominal DCE-MRI data at high temporal resolution and compare this method to a similar approach based on registration to the time series mean image in data from 8 patients. When the registration is driven by an appropriate model, we find significant improvements in model-fitting. Also, model-driven registration influences parameter estimates and reduces repeat study variability in measurements of blood volume.