Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction

Abstract

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

“…However, this is not always the case, for instance, if there is no prior knowledge of the sample composition, or low chemical concentration and the sample deposits are on the order of detector voxel size. In such cases, K-edges may be completely concealed by the background noise of a channelwise FBP reconstruction as shown in [ 6 ]. Hence, we need to rely on a more sophisticated reconstruction that has the ability to suppress noise in both the spatial and spectral domains and confidently identify and quantify the elemental distribution of each material.…”

“…This is particularly important when looking to perform further spectral analyses, such as the use of K-edge subtraction (KES), where we segment elemental phases based on identification of their K-edges. For a detailed task-based reconstruction quality assessment based on KES analysis, we refer the reader to [ 6 ], where we compare more advanced spatio-spectral reconstruction methods for a biological sample.…”

“…The scope of this paper is to present the capabilities of the Core Imaging Library (CIL) ( ), releases available at [ 4 , 5 ], of the collaborative computational project in tomographic imaging (CCPi) for multichannel tomography. It allows one to reconstruct higher quality images and ensure more accurate spatiospectral K-edge identification, see for instance [ 6 , 7 ], where novel reconstruction methods are introduced for laboratory-based hyperspectral CT and neutron tomography, respectively. CIL is an open source, object-oriented library (primarily written in Python) for processing tomographic data.…”

Low-rank flat-field correction for artifact reduction in spectral computed tomography

“…The quality of the computed reconstructions is assessed by visual inspection combined with contrast-to-noise ratio (CNR). The CNR metric is used for evaluating the image contrast and noise properties for a selected region of interest (ROI) [22]. We use the method proposed by Bian et al [23] where a ROI with low-contrast structure is compared to a background ROI while taking the standard deviations of both the signal and background ROIs into account.…”

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

“…Dataset: All the files of this study are freely available and can be downloaded from [22]. It contains a) 4D hyperspectral (energy-resolved) X-ray CT projection data, b) flat-field data, c) energy in keV for every energy bin and d) the cone-geometry setup.…”