title: Spatio-temporal registration of cardiac perfusion MRI exams using high-dimensional mutual information.
abstract: Compensating for cardio-thoracic motion artifacts in contrast-enhanced cardiac perfusion MRI (p-MRI) sequences is a key issue for the quantitative assessment of myocardial iscæmia. The classical paradigm consists of registering each sequence frame on some reference using an intensity-based matching criterion. In this paper, we present a novel unsupervised method for the groupwise registration of cardiac p-MRI exams based on mutual information between high-dimensional feature distributions. Specifically, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched to a target feature distribution derived from a registered reference template. Using consistent kth nearest neighbors entropy estimators further enables the variational optimization of the model over finite- and infinite-dimensional transform spaces. Experiments on simulated and natural datasets demonstrate its accuracy and relevance for the reliable assessment of regional perfusion.
authors.affiliation: ARTEMIS Department; CNRS UMR 8145; Institut TELECOM / TELECOM SudParis, Evry, France. Sameh.Hamrouni@it-sduparis.eu
authors.forename: Sameh
authors.initials: S
authors.lastname: Hamrouni
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rougon
authors.forename: Francoise
authors.initials: F
authors.lastname: Preteux
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D013997:Time Factors