title: Global assessment of cardiac function using image statistics in MRI.
abstract: The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation, which is a challenging and computationally expensive task that may result in high estimation errors. In this study, we propose to estimate the EF in real-time directly from image statistics using machine learning technique. From a simple user input in only one image, we build for all the images in a subject dataset (200 images) a statistic based on the Bhattacharyya coefficient of similarity between image distributions. We demonstrate that these statistics are non-linearly related to the LV cavity areas and, therefore, can be used to estimate the EF via an Artificial Neural Network (ANN) directly. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlate very well with those obtained from independent manual segmentations.
authors.affiliation: University of Western Ontario, London, Canada.
authors.forename: Mariam
authors.initials: M
authors.lastname: Afshin
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.forename: Ali
authors.initials: A
authors.lastname: Islam
authors.forename: Aashish
authors.initials: A
authors.lastname: Goela
authors.forename: Terry M
authors.initials: TM
authors.lastname: Peters
authors.forename: Shuo
authors.initials: S
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis*