pseudo-increasing frame rates of echocardiography images using manifold learning

background: increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. methods: in this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimensional manifold space. in this method we presume that the dimensionality of echocardiography images obtained from a patient is artificially high and the images can be described as functions of only a few underlying parameters such as periodic motion due to heartbeat. results: by this approach, each image is projected as a point on the reconstructed manifold; hence the relationship between images in the new domain can be obtained according to periodicity of the heart cycle. conclusions: to have a better tracking of the echocardiography images during the fast motions of heart we have rearranged the similar frames of consecutive heart cycles in a sequence. this provides a full view slow motion of heart movement through increasing the frame rate to three times the traditional ultrasound systems.