title: Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model.
abstract: Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.
authors.affiliation: Siemens AG, 91058 Erlangen, Germany. lynchm@eeng.dcu.ie
authors.forename: Michael
authors.initials: M
authors.lastname: Lynch
authors.forename: Ovidiu
authors.initials: O
authors.lastname: Ghita
authors.forename: Paul F
authors.initials: PF
authors.lastname: Whelan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006352:Heart Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008953:Models, Anatomic; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity