Automatic optimization of alignment parameters for tomography datasets

Abstract

As tomographic imaging is being performed at increasingly smaller scales, the stability of the scanning hardware is of great importance to the quality of the reconstructed image. Instabilities lead to perturbations in the geometrical parameters used in the acquisition of the projections. In particular for electron tomography and high-resolution X-ray tomography, small instabilities in the imaging setup can lead to severe artifacts. We present a novel alignment algorithm for recovering the true geometrical parameters after the object has been scanned, based on measured data. Our algorithm employs an optimization algorithm that combines alignment with reconstruction. We demonstrate that problem-specific design choices made in the implementation are vital to the success of the method. The algorithm is tested in a set of simulation experiments. Our experimental results indicate that the method is capable of aligning tomography datasets with considerably higher accuracy compared to standard cross-correlation methods. © 2013 Springer-Verlag.