  Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast
imaging of heterogeneous and structurally complicated objects is challenging
under laboratory conditions due to partial spatial coherence and
polychromaticity. A learning-based method (LBM) provides a non-linear approach
to this problem while not being constrained by restrictive assumptions about
object properties and beam coherence. In this work, a LBM was assessed for its
applicability under practical scenarios by evaluating its robustness and
generalizability under typical experimental variations. Towards this end, an
end-to-end LBM was employed for QPR under laboratory conditions and its
robustness was investigated across various system and object conditions. The
robustness of the method was tested via varying propagation distances and its
generalizability with respect to object structure and experimental data was
also tested. Although the LBM was stable under the studied variations, its
successful deployment was found to be affected by choices pertaining to data
pre-processing, network training considerations and system modeling. To our
knowledge, we demonstrated for the first time, the potential applicability of
an end-to-end learning-based quantitative phase retrieval method, trained on
simulated data, to experimental propagation-based x-ray phase contrast
measurements acquired under laboratory conditions. We considered conditions of
polychromaticity, partial spatial coherence, and high noise levels, typical to
laboratory conditions. This work further explored the robustness of this method
to practical variations in propagation distances and object structure with the
goal of assessing its potential for experimental use. Such an exploration of
any LBM (irrespective of its network architecture) before practical deployment
provides an understanding of its potential behavior under experimental
settings.
