Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer
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.
KeywordsSimilarity Measure Mutual Information Motion Correction Target Registration Error Medical Image Analysis
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