Separating edges from microstructure in X-ray dark-field imaging: Evolving and devolving perspectives via the X-ray Fokker-Planck equation

TL;DR

This paper introduces a novel algorithm based on the X-ray Fokker-Planck equation that can distinguish between microstructure and edge-induced contrast in X-ray dark-field imaging, improving image interpretation and reducing artifacts.

Contribution

It presents a new devolving perspective and algorithm for speckle-based X-ray imaging that separates different XDF contrast mechanisms, enhancing imaging capabilities.

Findings

The algorithm successfully separates microstructure and edge contrast in experimental data.

Devolving perspective improves differentiation of contrast mechanisms in XDF imaging.

Application reduces streaking artifacts in XDF computed tomography.

Abstract

A key contribution to X-ray dark-field (XDF) is X-ray diffusion by sample structures smaller than the imaging system's spatial resolution. However, some XDF techniques report that resolvable sample edges also generate XDF. Speckle-based X-ray imaging (SBXI) extracts XDF by analyzing sample-imposed changes to a reference speckle pattern's visibility. We present an algorithm for SBXI (a variant of our Multimodal Intrinsic Speckle-Tracking (MIST) algorithm) capable of separating these two distinct XDF contrast mechanisms. The algorithm uses the 'devolving' X-ray Imaging Fokker-Planck equation as its forward model and then solves the associated multimodal inverse problem, to retrieve sample attenuation, phase, and XDF. Previous MIST variants were based on the evolving Fokker-Planck equation, which considers how a reference-speckle image is modified by introducing a sample. The devolving perspective instead considers how the image collected in the presence of the sample and speckle membrane optically flows in reverse, to generate the reference-speckle image when the sample is removed from the system. We compare single- and multiple-exposure multimodal retrieval algorithms from the two Fokker-Planck perspectives. We demonstrate that the devolving perspective can distinguish between two physically different XDF contrast mechanisms; unresolved microstructure- and sharp-edge-induced XDF. This was verified by applying the retrieval algorithms to two experimental data sets. We anticipate that this work will be useful in: Yielding a pair of complementary XDF images that separate sharp-edge diffuse scatter from unresolved microstructure diffuse scatter. XDF computed tomography, where the strong edge XDF can lead to tainting streaking artefacts. Sample preparation, as samples will not need to be embedded since the strong XDF edge signal seen between the sample and air can be separated out.