Registration of phase‐contrast images in propagation‐based X‐ray phase tomography

Abstract

SummaryX-ray phase tomography aims at reconstructing the 3D electron density distribution of an object. It offers enhanced sensitivity compared to attenuation-based X-ray absorption tomography. In propagation-based methods, phase contrast is achieved by letting the beam propagate after interaction with the object. The phase shift is then retrieved at each projection angle, and subsequently used in tomographic reconstruction to obtain the refractive index decrement distribution, which is proportional to the electron density. Accurate phase retrieval is achieved by combining images at different propagation distances. For reconstructions of good quality, the phase-contrast images recorded at different distances need to be accurately aligned. In this work, we characterise the artefacts related to misalignment of the phase-contrast images, and investigate the use of different registration algorithms for aligning inline phase-contrast images. The characterisation of artefacts is done by a simulation study and comparison with experimental data. Loss in resolution due to vibrations is found to be comparable to attenuation-based computed tomography. Further, it is shown that registration of phase-contrast images is nontrivial due to the difference in contrast between the different images, and the often periodical artefacts present in the phase-contrast images if multilayer X-ray optics are used. To address this, we compared two registration algorithms for aligning phase-contrast images acquired by magnified X-ray nanotomography: one based on cross-correlation and one based on mutual information. We found that the mutual information-based registration algorithm was more robust than a correlation-based method.L. Weber and A. Hänsch contributed equally.Correspondence to: M. Langer,

X-ray in-line holography and holotomography at the NanoMAX beamline

“…Given that the propagation artifacts are different for each defocusing distance, performing a simple intensity registration approach was not possible. To address this issue, we used mutual information methods (Weber et al, 2018). The described process is implemented in PyPhase (Langer et al, 2021), a modular open-source phase-reconstruction package implemented in Python.…”

PyPhase – a Python package for X-ray phase imaging

“…This effect precludes simple correlation-based approaches in all but the most trivial cases. For this case, registration algorithms based on mutual information (Weber et al, 2018) seem to be more appropriate. Image registration is implemented as a class Registrator.…”

Nanoscopic X-ray imaging and quantification of the iron cellular architecture within single fibroblasts of Friedreich's ataxia patients

“…shows an X-ray in-line holographic image of the targeted fibroblast. X-ray in-line holography is an imaging technique in which the phase -reflecting the electron density of the sample -can be reconstructed from propagation-based phase-contrast images taken at different effective propagation distances (Stockmar et al, 2015;Weber et al, 2018;Stockmar et al, 2013;Cloetens et al, 2006). Achievable resolution with in-line holography may vary depending on the sample, but was estimated to be in the 100 nm range for our freeze-dried fibroblast cells.…”