fixed rb image and any possible vb endoluminal rendering a fairly straightforward approach to accomplish this is by employing a gauss newton gradient descent algorithm that attempts to minimize a difference measure between the rb image and the vb image with respect to the viewing parameters i e viewpoint of the virtual image the method for doing this is similar to the lucas kanade image alignment algorithm 5 the objective function used in 5 6 is the sum squared difference ssd between the pixel intensities of the two images although weighted ssd is equally viable and weighted or unweighted normalized cross correlation cc can be used if some additional weak assumptions are made using the ssd the objective function can thus be written as e u2211 u v u2062 i v u2061 u v p u03b4 u2062 u2062 p i r u2061 u v 2 1 where p is the vector of viewing parameters i v u v p u03b4p is the virtual vb image rendered from the viewpoint p u03b4p u and v are the row and column indices and i r is the real rb image following the procedure of 5 it is shown that that the gauss newton parameter update u03b4p can be found as u03b4 u2062 u2062 p h 1 u2062 u2211 u v u2062 u2202 i u2202 p u2061 i v u2061 u v p i.