# Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions

arXiv: https://arxiv.org/abs/2211.01372 · HTML: https://mlsanity.com/paper/2211.01372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 77 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Low Photon Count Phase Retrieval Using Deep Learning](https://mlsanity.com/paper/1806.10029/) (2018)
- [Real-time coherent diffraction inversion using deep generative networks](https://mlsanity.com/paper/1806.03992/) (2018)
- [PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasets](https://mlsanity.com/paper/2011.08660/) (2020)
- [Boosting spatial resolution by incorporating periodic boundary conditions into single-distance hard-x-ray phase retrieval](https://mlsanity.com/paper/2005.03660/) (2020)
- [Constrained Non-Linear Phase Retrieval for Single Distance X-ray Phase Contrast Tomography](https://mlsanity.com/paper/2009.10324/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)

## Abstract (arXiv)

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.