  For reconstructing large tomographic datasets fast, filtered
backprojection-type or Fourier-based algorithms are still the method of choice,
as they have been for decades. These robust and computationally efficient
algorithms have been integrated in a broad range of software packages. The
continuous mathematical formulas used for image reconstruction in such
algorithms are unambiguous. However, variations in discretisation and
interpolation result in quantitative differences between reconstructed images,
and corresponding segmentations, obtained from different software. This hinders
reproducibility of experimental results, making it difficult to ensure that
results and conclusions from experiments can be reproduced at different
facilities or using different software.
  In this paper, we propose a way to reduce such differences by optimising the
filter used in analytical algorithms. These filters can be computed using a
wrapper routine around a black-box implementation of a reconstruction
algorithm, and lead to quantitatively similar reconstructions. We demonstrate
use cases for our approach by computing implementation-adapted filters for
several open-source implementations and applying it to simulated phantoms and
real-world data acquired at the synchrotron. Our contribution to a reproducible
reconstruction step forms a building block towards a fully reproducible
synchrotron tomography data processing pipeline.
