  Here we apply hyperspectral bright field imaging to collect computed
tomographic images with excellent energy resolution (800 eV), 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.
