Efficient implementation of a local tomography reconstruction algorithm.

We propose an efficient implementation of an interior tomography reconstruction method based on a known subregion. This method iteratively refines a reconstruction, aiming at reducing the local tomography artifacts. To cope with the ever increasing data volumes, this method is highly optimized on two aspects: firstly, the problem is reformulated to reduce the number of variables, and secondly, the operators involved in the optimization algorithms are efficiently implemented. Results show that [Formula: see text] slices can be processed in tens of seconds, while being beyond the reach of equivalent exact local tomography method.

A practical local tomography reconstruction algorithm based on a known sub-region.

A new method to reconstruct data acquired in a local tomography setup is proposed. This method uses an initial reconstruction and refines it by correcting the low-frequency artifacts, known as the cupping effect. A basis of Gaussian functions is used to correct the initial reconstruction. The coefficients of this basis are found by optimizing iteratively a fidelity term under the constraint of a known sub-region. Using a coarse basis reduces the degrees of freedom of the problem while actually correcting the cupping effect. Simulations show that the known region constraint yields an unbiased reconstruction, in accordance with uniqueness theorems stated in local tomography.

Ring artifacts correction in compressed sensing tomographic reconstruction.

Ring artifacts are a very common problem in tomographic reconstruction, and numerous methods exist to either pre-process the sinogram or correct the reconstructed slice. A novel approach to perform the correction as part of the reconstruction process is presented. It is shown that for iterative techniques, which amount to optimizing an objective function, the ring artifacts correction can be easily integrated in the formalism, enabling simultaneous slice reconstruction and ring artifacts correction. This method is tested and compared with mainstream correction techniques for both simulated and experimental data. Results show that the correction is efficient, especially for undersampled datasets. This technique is included in the PyHST2 code which is used at the European Synchrotron Radiation Facility for tomographic reconstruction.

Image stack alignment in full-field X-ray absorption spectroscopy using SIFT_PyOCL.

Full-field X-ray absorption spectroscopy experiments allow the acquisition of millions of spectra within minutes. However, the construction of the hyperspectral image requires an image alignment procedure with sub-pixel precision. While the image correlation algorithm has originally been used for image re-alignment using translations, the Scale Invariant Feature Transform (SIFT) algorithm (which is by design robust versus rotation, illumination change, translation and scaling) presents an additional advantage: the alignment can be limited to a region of interest of any arbitrary shape. In this context, a Python module, named SIFT_PyOCL, has been developed. It implements a parallel version of the SIFT algorithm in OpenCL, providing high-speed image registration and alignment both on processors and graphics cards. The performance of the algorithm allows online processing of large datasets.