Maximum likelihood x-ray computed tomography with real data

We present experimental results for a maximum likelihood (ML) reconstruction algorithm for x-ray computed tomography (CT) which incorporates both the Poisson nature of photon counts and the finite width of the x-ray beam. Count data, obtained from an industrial CT scanner, are used to reconstruct an image of a concrete rebar-reinforced cube. The internal structure of the cube is reconstructed using both ML and filtered backprojection (FBP). We find that the ML method reduces noise and streak artifacts in the reconstructed image thus supporting our earlier work with simulated count data.