title: Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer.
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 datasets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.
authors: Institute of Biomedical Engineering, Oxford University, UK.
authors: Manav
authors: M
authors: Bhushan
authors: Julia A
authors: JA
authors: Schnabel
authors: Laurent
authors: L
authors: Risser
authors: Mattias P
authors: MP
authors: Heinrich
authors: J Michael
authors: JM
authors: Brady
authors: Mark
authors: M
authors: Jenkinson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D016011:Normal Distribution; D011336:Probability; D011859:Radiography
chemical_list: D003287:Contrast Media