Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction

Ryan Warr, Evelina Ametova, Robert Cernik, Gemma Fardell, Stephan Handschuh, Jakob Jorgensen, Evangelos Papoutsellis, Edoardo Pasca, Philip Withers

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number20818
JournalScientific Reports
Issue number1
Publication statusPublished - 21 Oct 2021


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