Alignment methods for nanotomography with deep sub-pixel accuracy
<p>This repository contains codes for alignment of projections for tomography and laminography. Additionally we include artificial, i.e. simulated, and experimental examples.</p> <p><strong>Here you will find:</strong></p> <p>1) <em>cSAXS_matlab_tomo_shared.zip</em> - Codes and routines, as introduced in [1], for deep sub-pixel alignment and reconstruction of tomography and laminography datasets.</p> <p>2) <em>example_data.mat</em> - artificial tomography dataset that serves as an example for the alignment codes.</p> <p><br> <strong>Citation and acknowledgements</strong></p> <p>If you use the codes from this repository here is where to find more information and the expected citation.</p> <p>For use or further development of the tomography and laminography alignment and reconstruction codes please cite:<br> [1] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</p> <p><br> <strong>Code requirements:</strong></p> <p>Reconstruction scripts were tested for Matlab2018a with parallel toolkit, CUDA 9.0. NVIDIA, RHEL 7.6<br> GPU is required for the tomographic reconstruction.</p> <p><br> <strong>Instalation and reconstruction:</strong></p> <p> 1) Download example datasets and code from https://doi.org/10.5281/zenodo.3539550 or a measured ptychotomography dataset from https://doi.org/10.5281/zenodo.3539513<br> 2) Make sure that the downloaded datasets can be loaded by matlab<br> 3) Choose one of the provided datasets in the "run_simple_example.m" template<br> 4) Run script "run_simple_example.m". The final results will be stored in folder defined in par.output_folder</p> <p><br> <strong>Example dataset</strong> contains following two variables:</p> <p><em>stack_object</em> - complex valued unaligned projection with dimensions [Npix_vertical , Npix_horizontal, number_of_angles]</p> <p> <em>theta</em> - vector of corresponding projection angles in degrees</p> <p> </p> <p>This code and subroutines are part of a continuous development. There is no liability on PSI or cSAXS. License for the codes can be found in the individual scripts.</p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 8
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0