Maximum a posteriori optimization-a method for calculation of dynamic changes in ventricular contours from angiographic image sequences

A robust and accurate method for automatic determination of dynamic left ventricular function has been developed for application to sequences of angiographic images. The method employs a maximum a posteriori optimization approach. The shape of the ventricle boundary is modeled by an elliptical Fourier series. The intensity cross section of the ventricle boundary is modeled using a derivative of a Gaussian. Prior knowledge of the probability of a given ventricle shape and temporal behavior is encoded using a distribution on the parameters. This prior knowledge is coupled with the spatial and temporal information in the image sequence using a guided gradient descent technique to estimate the dynamic contour coefficients. The results illustrate the performance of the optimization algorithm on two examples of digital subtraction X-ray ventriculograms