Improving Interior Tomography Reconstruction Based On Prior Knowledge

We address the interior problem of computed tomography that occurs when projection data is only available for a region in the interior of the sample. In this case, it is not possible to accurately reconstruct the attenuation function even in the interior domain. We consider an algorithm for correcting the interior tomography reconstruction which is based on prior knowledge in the interior domain. This correction algorithm is evaluated by performing numerical experiments with the Shepp-Logan phantom for various amounts of data loss, noise in the available projection data, various values of the attenuation function known a priori, and various positions within the sample where the prior information is available. Good performance of the algorithm based on prior knowledge at one point is demonstrated in the case of noiseless data. In the presence of noise in the projection data, improvements in the reconstructed attenuation function are obtained based on prior knowledge at a number of points in the interior domain. The robustness of the correction algorithm to errors in the values of the attenuation function used as prior knowledge was also investigated.