Chapter

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011

Volume 6891 of the series Lecture Notes in Computer Science pp 476-483

Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer

  • Manav BhushanAffiliated withLancaster UniversityCarnegie Mellon UniversityInstitute of Biomedical Engineering, Oxford UniversityCentre for Functional MRI of the Brain, Oxford University
  • , Julia A. SchnabelAffiliated withLancaster UniversityInstitute of Biomedical Engineering, Oxford University
  • , Laurent RisserAffiliated withLancaster UniversityInstitute of Biomedical Engineering, Oxford University
  • , Mattias P. HeinrichAffiliated withLancaster UniversityCarnegie Mellon UniversityInstitute of Biomedical Engineering, Oxford UniversityCentre for Functional MRI of the Brain, Oxford University
  • , J. Michael BradyAffiliated withCarnegie Mellon UniversityDepartment of Radiation Oncology and Biology, Oxford University
  • , Mark JenkinsonAffiliated withCarnegie Mellon UniversityCentre for Functional MRI of the Brain, Oxford University

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Abstract

We present a novel Bayesian framework for non-rigid motion correction and pharmacokinetic parameter estimation in dceMRI sequences which incorporates a physiological image formation model into the similarity measure used for motion correction. The similarity measure is based on the maximization of the joint posterior probability of the transformations which need to be applied to each image in the dataset to bring all images into alignment, and the physiological parameters which best explain the data. The deformation framework used to deform each image is based on the diffeomorphic logDemons algorithm. We then use this method to co-register images from simulated and real dceMRI data-sets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.