Phase retrieval beyond the homogeneous object assumption for X-ray in-line holographic imaging

TL;DR

This paper introduces a novel phase retrieval algorithm for X-ray in-line holography that does not depend on the homogeneous object assumption, enhancing image quality in biomedical imaging.

Contribution

The authors present a generalized phase retrieval algorithm that relaxes the homogeneous object assumption, improving reconstruction quality over traditional CTF-based methods.

Findings

Superior reconstruction quality compared to CTF-based methods

Stable performance demonstrated through experimental comparison

Applicable to biomedical near-field holography

Abstract

X-ray near field holography has proven to be a powerful 2D and 3D imaging technique with applications ranging from biomedical research to material sciences. To reconstruct meaningful and quantitative images from the measurement intensities, however, it relies on computational phase retrieval which in many cases assumes the phase-shift and attenuation coefficient of the sample to be proportional. Here, we demonstrate an efficient phase retrieval algorithm that does not rely on this homogeneous-object assumption and is a generalization of the well-established contrast-transfer-function (CTF) approach. We then investigate its stability and present an experimental study comparing the proposed algorithm with established methods. The algorithm shows superior reconstruction quality compared to the established CTF-based method at similar computational cost. Our analysis provides a deeper fundamental understanding of the homogeneous object assumption and the proposed algorithm will help improve the image quality for near-field holography in biomedical applications