Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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 datasets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.

Type

Journal article

Journal

Med Image Comput Comput Assist Interv

Publication Date

2011

Volume

14

Pages

476 - 483

Keywords

Algorithms, Bayes Theorem, Colorectal Neoplasms, Computer Simulation, Contrast Media, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Normal Distribution, Probability, Radiography