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In this paper, we consider prior-based dimension reduction Kalman filter forundersampled dynamic X-ray tomography. With this method, the X-rayreconstructions are parameterized by a low-dimensional basis. Thus, theproposed method is a) computationally very light; and b) extremely robust asall the computations can be done explicitly. With real and simulatedmeasurement data, we show that the method provides accurate reconstructionseven with very limited number of angular directions.

Undersampled dynamic X-ray tomography with dimension reduction Kalman filter