Mathematical scripting languages are commonly used to develop new tomographic reconstruction algorithms. For large experimental datasets, high performance parallel (GPU) implementations are... Show moreMathematical scripting languages are commonly used to develop new tomographic reconstruction algorithms. For large experimental datasets, high performance parallel (GPU) implementations are essential, requiring a re-implementation of the algorithm using a language that is closer to the computing hardware. In this paper, we introduce a new MATLAB interface to the ASTRA toolbox, a high performance toolbox for building tomographic reconstruction algorithms. By exposing the ASTRA linear tomography operators through a standard MATLAB matrix syntax, existing and new reconstruction algorithms implemented in MATLAB can now be applied directly to large experimental datasets. This is achieved by using the Spot toolbox, which wraps external code for linear operations into MATLAB objects that can be used as matrices. We provide a series of examples that demonstrate how this Spot operator can be used in combination with existing algorithms implemented in MATLAB and how it can be used for rapid development of new algorithms, resulting in direct applicability to large-scale experimental datasets. Show less
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... Show moreTomography 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. Show less
