In Computed Tomography, it is often standard to scan an object with the projection angles spread equidistantly over the full rotation of the object. If the number of projection angles is relatively...Show moreIn Computed Tomography, it is often standard to scan an object with the projection angles spread equidistantly over the full rotation of the object. If the number of projection angles is relatively small (e.g. because of radiation damage), the choice of the distribution of these angles is important. Especially if the object has certain main directions, in which the most important features are aligned, the reconstruction can be improved by selecting angles in these directions. In this thesis, possibilities for improving the choice of projection angles are investigated. Three angle selection algorithms are discussed: a greedy algorithm, a coordinate descent algorithm and an adaptation of the ensemble Kalman Filter algorithm. The algorithms try to find the minimum of a cost function, which is based on the L 2 -norm. The performance of these algorithms is shown on three computer generated phantoms and one real-world image, generated from scanning a wooden tree stem in the FleX-Ray scanner at CWI. The results of choosing angles with the algorithms are compared with choosing equidistant projection angles. The results show that especially the coordinate descent method is capable of finding projection angles that lower the cost function. In real life situations the true image is not available. Therefore, the possibility of using training data to estimate the cost function in that case, is investigated. We assume that these training samples come from the same probability distribution as the true image. Then, averaging over the cost function of these training samples improves the choice of projection angles with respect to equidistantly chosen angles.Show less
