  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. Despite
the fact that the underlying mathematical formulas used for image
reconstruction are unambiguous, variations in discretisation and interpolation
result in quantitative differences between reconstructed images obtained from
different software. This hinders reproducibility of experimental results.
  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.
