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

Objective.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 deep learning-based method (DLBM) provides a nonlinear approach to this problem while not being constrained by restrictive assumptions about object properties and beam coherence. The objective of this work is to assess a DLBM for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.Approach.Towards this end, an end-to-end DLBM 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.Main results.Although the end-to-end DLBM 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.Significance.To our knowledge, we demonstrated for the first time, the potential applicability of an end-to-end learning-based QPR method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements acquired under laboratory conditions with a commercial x-ray source and a conventional detector. 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 DLBM (irrespective of its network architecture) before practical deployment provides an understanding of its potential behavior under experimental settings.

Looking Inside a Prototype Compact X-Ray Tube Comprising CNT-Based Cold Cathode and Transmission-Type Anode.

In this article, we offer a look inside our prototype compact X-ray tube by reporting on our findings when we experimentally studied it. We studied the prototype experimentally to characterize its primary components, i.e., carbon nanotube (CNT)-based cold cathode, electrostatic lens and transmission-type anode, and to validate our previous simulation studies. We characterized the CNT-based cold cathode by studying the relationship between the electron emission current and its control parameter, electron extraction voltage. This relationship, commonly known as the current-voltage characteristic, showed an exponential-like nature that is expected from the Fowler-Nordheim model for field emission. Next, we characterized the electrostatic lens by studying the effects of lens voltage on the focal spot size. Their relationship showed a "V" trend and corroborated that we can control the focal spot size by controlling the lens voltage. We then characterized the transmission-type anode of the prototype by studying its output X-ray energy spectra at different anode voltages. We could control the highest and the mean X-ray energies generated from the transmission-type anode using the anode voltage. For the same anode voltage and aluminum filtration, when we compared the Xray energy spectrum generated from the transmission-type anode with that of the conventional reflection-type anode, we observed that the two energy spectra agreed with each other.