  In this paper, we consider prior-based dimension reduction Kalman filter for
undersampled dynamic X-ray tomography. With this method, the X-ray
reconstructions are parameterized by a low-dimensional basis. Thus, the
proposed method is a) computationally very light; and b) extremely robust as
all the computations can be done explicitly. With real and simulated
measurement data, we show that the method provides accurate reconstructions
even with very limited number of angular directions.
