As tomographic imaging is being performed at increasingly smaller scales, the stability of the scanning hardware is of great impor- tance to the quality of the reconstructed image. Instabilities lead to per- turbations 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 se- vere 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 align- ing tomography datasets with considerably higher accuracy compared to standard cross-correlation methods. Tomography deals with the problem of reconstructing an object from projections (1). Projections are measured by a scanning device at varying orientations with respect to the object. Each projection consists of measurements for a set of par- allel lines, where the line integral of the object density is measured. By rotating around the object and creating a large number of projections, enough informa- tion is gathered to reconstruct an object that resembles the ground truth. The result is an ill conditioned inverse problem that can be solved using numerical methods. Tomography has a wide range of applications, ranging from industrial quality control of large objects using X-rays down to imaging of nanomaterials by elec- tron microscopy. In particular at the smallest scales, problems with the alignment of the projection data form a key bottleneck for the quality of the reconstructed image. The geometry that is presumed in the reconstruction process is different from the actual geometry of the projection acquisition. In electron tomography, the specimen has to be recentered for each recorded image as the sample stage is not eucentric, causing lateral shifts in the projection
['Folkert Bleichrodt', 'K. Joost Batenburg']
Automatic Optimization of Alignment Parameters for Tomography Datasets