Improving robustness of tomographic reconstruction methods
Promotor: Prof.dr. K.J. Batenburg
- F. Bleichrodt
- 10 November 2015
- Thesis in Leiden Repository
Tomography is an imaging technique for reconstructing an object from projection images. Projection images are obtained by a scanner consisting of a radiation source and a detector. Due to imperfections in the scanner setup, measurement errors are introduced in the projections, which lead to errors in the reconstructed image. In this thesis we developed several techniques to make reconstruction algorithms more robust to these imperfections, such that errors in the projection data have a smaller effect on the reconstruction. We consider errors caused by: misalignment, low radiation dose, unknown background intensities and nonlinearities in the projection acquisition. The key concept that is used for correcting errors is consistency optimization of the projection data and the reconstruction. By using a forward projection model, projections from a reconstruction can be simulated. If the model (and the reconstruction) is accurate the simulated projections match the observed projections. Consistency is therefore a measure which we can use to estimate parameters of the model. For example, misalignment can be corrected for by introducing geometric parameters for the position of the detector and perturbations in the projection angles. Once the parameters correspond to the experiment the consistency is maximized and reconstruction errors are reduced.