Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography.

Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of multiple contrast agents across different tissue structures. Here we describe a hyperspectral imaging routine for distinguishing multiple chemical agents, regardless of contrast similarity. Using a set of elemental calibration phantoms, we perform a first instance of direct stain concentration measurement using spectral absorption edge markers. Applied to a set of double- and triple-stained biological specimens, the study analyses the extent of stain overlap and uptake regions for commonly used contrast markers. An improved understanding of stain concentration as a function of position, and the interaction between multiple stains, would help inform future studies on multi-staining procedures, as well as enable future exploration of heavy metal uptake across medical, agricultural and ecological fields.

High-energy synchrotron X-ray tomography coupled with digital image correlation highlights likely failure points inside ITER toroidal field conductors.

Two sections of heat-treated (HT) and non-heat-treated (NHT) Cable-in-Conduit Conductor (CICC) of a design similar to the ITER tokomak have been imaged using very high energy X-ray tomography at the ESRF beamline ID19. The sample images were collected at four temperatures down to 77 K. These results showed a greater degree of movement, bundle distortion and touching strands in the NHT sample. The HT sample showed non-linear movements with temperature especially close to 77 K; increasing non-circularity of the superconducting fibre bundles towards the periphery of the CICC, and touching bundles throughout the CICC. The images have highlighted where future design might improve potential weakness, in particular at the outer perimeters of the conductor and the individual sub-cable, 'petal' wraps.

Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction.

Here we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (~ 1 keV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achieve high quality energy-dispersive tomograms from low dose, noisy datasets using a dedicated iterative reconstruction algorithm. This exploits the spatial smoothness and inter-channel structural correlation in the spectral domain using two carefully chosen regularisation terms. For a multi-phase phantom, a reduction in scan time of 36 times is demonstrated. Spectral analysis methods including K-edge subtraction and absorption step-size fitting are evaluated for an ex vivo, single (iodine)-stained biological sample, where low chemical concentration and inhomogeneous distribution can affect soft tissue segmentation and visualisation. The reconstruction algorithms are available through the open-source Core Imaging Library. Taken together, these tools offer new capabilities for visualisation and elemental mapping, with promising applications for multiply-stained biological specimens.

Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography.

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.