Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction

TL;DR

This paper introduces an advanced hyperspectral tomography method that significantly reduces scan time and improves image quality for bioimaging by using a novel iterative reconstruction algorithm that leverages spectral and spatial correlations.

Contribution

It presents a new iterative reconstruction algorithm for hyperspectral tomography that enhances image quality from low-dose noisy data, enabling faster scans and better elemental mapping in biological samples.

Findings

36-fold reduction in scan time for phantom imaging

High-quality energy-dispersive tomograms from low-dose datasets

Effective spectral analysis for biological sample visualization

Abstract

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.