Optimization of Data Acquisition in Tomography Using Kalman Estimation Filter

Tomography is the process of reconstructing three dimensional images from two-dimensional projections. In general, this process has two separate phases: data acquisition and image reconstruction. This article concentrates on the optimization of the first phase via using the tools of estimation theory and specifically the Kalman estimation filter. We demonstrate that by choosing the right measurement matrix in Kalman filter, we can maximize the information content of each measurement and reconstruct images of the same quality making less number of measurements or by using less number of sources/detectors.