Optimal dynamic tomography for wide-sense stationary spatial random fields
Mark David Butala, Farzad Kamalabadi · 2009
Dynamic tomography is concerned with the image formation of a temporally changing object from its line integral projections. The problem remains challenging because of its high dimensionality. In this paper, we identify a sufficient class of dynamic tomography problems that can be solved by a state estimator that requires only linear shift-invariant filtering operations. This class includes rigid-body motion, common in biomedical imaging scenarios. The new state estimator is far less computationally demanding than classic methods such as the Kalman filter. Whereas the Kalman filter requires O(N2) memory storage and O(N3) processing for anN-dimensional problem, the state estimator derived in this work requires only O(N) storage and O(N logN) processing.