X-ray tomographic reconstruction and segmentation pipeline for the long-wavelength macromolecular crystallography beamline at Diamond Light Source.

In this paper a practical solution for the reconstruction and segmentation of low-contrast X-ray tomographic data of protein crystals from the long-wavelength macromolecular crystallography beamline I23 at Diamond Light Source is provided. The resulting segmented data will provide the path lengths through both diffracting and non-diffracting materials as basis for analytical absorption corrections for X-ray diffraction data taken in the same sample environment ahead of the tomography experiment. X-ray tomography data from protein crystals can be difficult to analyse due to very low or absent contrast between the different materials: the crystal, the sample holder and the surrounding mother liquor. The proposed data processing pipeline consists of two major sequential operations: model-based iterative reconstruction to improve contrast and minimize the influence of noise and artefacts, followed by segmentation. The segmentation aims to partition the reconstructed data into four phases: the crystal, mother liquor, loop and vacuum. In this study three different semi-automated segmentation methods are experimented with by using Gaussian mixture models, geodesic distance thresholding and a novel morphological method, RegionGrow, implemented specifically for the task. The complete reconstruction-segmentation pipeline is integrated into the MPI-based data analysis and reconstruction framework Savu, which is used to reduce computation time through parallelization across a computing cluster and makes the developed methods easily accessible.

Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction.

X-ray imaging applications in medical and material sciences are frequently limited by the number of tomographic projections collected. The inversion of the limited projection data is an ill-posed problem and needs regularization. Traditional spatial regularization is not well adapted to the dynamic nature of time-lapse tomography since it discards the redundancy of the temporal information. In this paper, we propose a novel iterative reconstruction algorithm with a nonlocal regularization term to account for time-evolving datasets. The aim of the proposed nonlocal penalty is to collect the maximum relevant information in the spatial and temporal domains. With the proposed sparsity seeking approach in the temporal space, the computational complexity of the classical nonlocal regularizer is substantially reduced (at least by one order of magnitude). The presented reconstruction method can be directly applied to various big data 4D (x, y, z+time) tomographic experiments in many fields. We apply the proposed technique to modelled data and to real dynamic X-ray microtomography (XMT) data of high resolution. Compared to the classical spatio-temporal nonlocal regularization approach, the proposed method delivers reconstructed images of improved resolution and higher contrast while remaining significantly less computationally demanding.

Chitosan Cryogels Cross-Linked with 1,1,3-Triglycidyloxypropane: Mechanical Properties and Cytotoxicity for Cancer Cell 3D Cultures.

Here, we have presented a new method of 1,1,3-triglycidyloxypropane (TGP) synthesis and investigated how cross-linker branching affects mechanical properties and cytotoxicity of chitosan scaffolds in comparison with those cross-linked using diglycidyl ethers of 1,4-butandiol (BDDGE) and poly(ethylene glycol) (PEGDGE). We have demonstrated that TGP is an efficient cross-linker for chitosan at a subzero temperature at TGP:chitosan molar ratios from 1:1 to 1:20. Although the elasticity of chitosan scaffolds increased in the following order of the cross-linkers PEGDGE > TGP > BDDGE, TGP provided cryogels with the highest compressive strength. Chitosan-TGP cryogels have shown low cytotoxicity for colorectal cancer HCT 116 cell line and supported the formation of 3D multicellular structures of the spherical shape and size up to 200 µm, while in more brittle chitosan-BDDGE cryogel this cell culture formed epithelia-like sheets. Hence, the selection of the cross-linker type and concentration for chitosan scaffold fabrication can be used to mimic the solid tumor microenvironment of certain human tissue, control matrix-driven changes in the morphology of cancer cell aggregates, and facilitate long-term experiments with 3D tumor cell cultures.

An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging.

The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.

Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization.

In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods.

An anatomically driven anisotropic diffusion filtering method for 3D SPECT reconstruction.

In this study, we aim to reconstruct single-photon emission computed tomography images using anatomical information from magnetic resonance imaging as a priori knowledge about the activity distribution. The trade-off between anatomical and emission data is one of the main concerns for such studies. In this work, we propose an anatomically driven anisotropic diffusion filter (ADADF) as a penalized maximum likelihood expectation maximization optimization framework. The ADADF method has improved edge-preserving denoising characteristics compared to other smoothing penalty terms based on quadratic and non-quadratic functions. The proposed method has an important ability to retain information which is absent in the anatomy. To make our approach more stable to the noise-edge classification problem, robust statistics have been employed. Comparison of the ADADF method is performed with a successful anatomically driven technique, namely, the Bowsher prior (BP). Quantitative assessment using simulated and clinical neuroreceptor volumetric data show the advantage of the ADADF over the BP. For the modelled data, the overall image resolution, the contrast, the signal-to-noise ratio and the ability to preserve important features in the data are all improved by using the proposed method. For clinical data, the contrast in the region of interest is significantly improved using the ADADF compared to the BP, while successfully eliminating noise.