title: Trans-dimensional MCMC methods for fully automatic motion analysis in tagged MRI.
abstract: Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.
authors: Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
authors: Ihor
authors: I
authors: Smal
authors: Noemí
authors: N
authors: Carranza-Herrezuelo
authors: Stefan
authors: S
authors: Klein
authors: Wiro
authors: W
authors: Niessen
authors: Erik
authors: E
authors: Meijering
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001499:Bayes Theorem; D006321:Heart / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D009010:Monte Carlo Method; D009038:Motion; D009206:Myocardium / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D015203:Reproducibility of Results