Spatial regularization applied to factor analysis of medical image sequences (FAMIS)

Dynamic image sequences allow physiological mechanisms to be monitored afte r the injection of a tracer. Factor analysis of medical image sequences (FA MIS) hence creates a synthesis of the information in one image sequence. It estimates a limited number of structures (factor images) assuming that the tracer kinetics (factors) are similar at each point inside the structure. A spatial regularization method for computing factor images (REG-FAMIS) is proposed to remove irregularities due to noise in the original data while p reserving discontinuities between structures. REG-FAMIS has been applied to two sets of simulations: (a) dynamic data with Gaussian noise and (b) dyna mic studies in emission tomography (PET or SPECT), which respect real tomog raphic acquisition parameters and noise characteristics. Optimal regulariza tion parameters are estimated in order to minimize the distance between ref erence images and regularized factor images. Compared with conventional fac tor images, the root mean square error between regularized images and refer ence factor images is improved by 3 for the first set of simulations, and b y about 1.5 for the second set of simulations. In all cases, regularized fa ctor images are qualitatively and quantitatively improved.