There is a newer version of the record available.

Published August 5, 2019 | Version v1

Alignment methods for nanotomography with deep sub-pixel accuracy

  • 1. Paul Scherrer Institute

Description

Artifical dataset that serves as an example for tomography alignment toolkit developed at cSAXS group, Paul Scherrer Institute for reconstruction and alignment of tomography and laminography datasets.

Example of simulated data are loaded from 'example_data.mat' that contains two
variables:
  stack_object - complex valued unaligned projection with following dimensions [Npix_vertical , Npix_horizontal, number_of_angles]
  theta - vector of corresponding projection angles in degrees

The other useful parameters are described and can be modified in the
following section.

Requirements:
  Reconstruction scripts were tested for Matlab2018a and CUDA 9.0. NVIDIA
  GPU is required for the tomographic reconstruction.
Instalation and reconstruction:
  1) Our tomography toolkit depends on cSAXS base script package: https://www.psi.ch/en/sls/csaxs/software
  2) Unpack cSAXS_matlab_base into folder where this script is placed
  3) Make sure that our example dataset 'example_data.mat' can be loaded by matlab
  4) Run script called "run_simple_example.m", the final results will be stored in folder defined in par.output_folder

This code and subroutines are part of a continuous development. There is no
liability on PSI or cSAXS.

Publications most relevant to this code
M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,
"Alignment methods for nanotomography with deep sub-pixel accuracy", Optics Express, 2019
M. Guizar-Sicairos, A. Diaz, M. Holler, M. S. Lucas, A. Menzel, R. A. Wepf, and O. Bunk
"Phase tomography from x-ray coherent diffractive imaging projections," Opt. Express 19, 21345-21357 (2011).
For tomographic consistency alignment:
M. Guizar-Sicairos, J. J. Boon, K. Mader, A. Diaz, A. Menzel, and O. Bunk,
"Quantitative interior x-ray nanotomography by a hybrid imaging technique," Optica 2, 259-266 (2015).

 

Files

Files (240.5 MB)

Name Size Download all
md5:fdb6b32a973092897a7798459c82de76
240.5 MB Download