Full Paper

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

  1. Giovanni A. Buonaccorsi1,*,
  2. James P.B. O'Connor1,2,
  3. Angela Caunce1,
  4. Caleb Roberts1,
  5. Sue Cheung1,
  6. Yvonne Watson1,
  7. Karen Davies1,
  8. Lynn Hope2,
  9. Alan Jackson1,
  10. Gordon C. Jayson2,
  11. Geoffrey J.M. Parker1

Article first published online: 29 OCT 2007

DOI: 10.1002/mrm.21405

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 58, Issue 5, pages 1010–1019, November 2007

Additional Information

How to Cite

Buonaccorsi, G. A., O'Connor, J. P., Caunce, A., Roberts, C., Cheung, S., Watson, Y., Davies, K., Hope, L., Jackson, A., Jayson, G. C. and Parker, G. J. (2007), Tracer kinetic model–driven registration for dynamic contrast-enhanced MRI time-series data. Magnetic Resonance in Medicine, 58: 1010–1019. doi: 10.1002/mrm.21405

Author Information

  1. 1

    Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK

  2. 2

    Department of Medical Oncology, Cancer Research UK, Christie Hospital, Manchester, UK

*Imaging Science and Biomedical Engineering, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK

Publication History

  1. Issue published online: 29 OCT 2007
  2. Article first published online: 29 OCT 2007
  3. Manuscript Accepted: 1 AUG 2007
  4. Manuscript Revised: 25 JUL 2007
  5. Manuscript Received: 4 DEC 2006

Funded by

  • Cancer Research UK. Grant Numbers: C237/A6295, C19221/A6086

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Keywords:

  • DCE-MRI;
  • cancer;
  • image registration;
  • contrast agent;
  • tracer kinetic modeling

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. Magn Reson Med 58:1010–1019, 2007. © 2007 Wiley-Liss, Inc.

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