Super-resolution for computed tomography based on discrete tomography

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Objective.This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).Approach.With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method.Main Results.The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.Significance.In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

Laboratory X-ray computed tomography imaging protocol allowing the operando investigation of electrode material evolution in various environments

A robust imaging protocol utilizing laboratory XCT is presented. Hybrid 2D/3D imaging at different scales with real-time monitoring allowed to assess, in operation, the evolution of zinc electrodes within three environments, namely alkaline, near-neutral, and mildly acidic. Different combinations of currents were used to demonstrate various scenarios exhibiting both dendritic and smooth deposition of active material. Directly from radiograms, the volume of the electrode and therefore its growth/dissolution rate was estimated and compared against tomographic reconstructions and theoretical values. The protocol combines simplistic cell design with multiple three-dimensional and two-dimensional acquisitions at different magnifications, providing a unique insight into electrode's morphology evolution within various environments.

A Shrewd Inspection of Vertebral Regionalization in Large Shrews (Soricidae: Crocidurinae)

The regionalization of the mammalian spinal column is an important evolutionary, developmental, and functional hallmark of the clade. Vertebral column regions are usually defined using transitions in external bone morphology, such as the presence of transverse foraminae or rib facets, or measurements of vertebral shape. Yet the internal structure of vertebrae, specifically the trabecular (spongy) bone, plays an important role in vertebral function, and is subject to the same variety of selective, functional, and developmental influences as external bone morphology. Here, we investigated regionalization of external and trabecular bone morphology in the vertebral column of a group of shrews (family Soricidae). The primary goals of this study were to: (1) determine if vertebral trabecular bone morphology is regionalized in large shrews, and if so, in what configuration relative to external morphology; (2) assess correlations between trabecular bone regionalization and functional or developmental influences; and (3) determine if external and trabecular bone regionalization patterns provide clues about the function of the highly modified spinal column of the hero shrew Scutisorex. Trabecular bone is regionalized along the soricid vertebral column, but the configuration of trabecular bone regions does not match that of the external vertebral morphology, and is less consistent across individuals and species. The cervical region has the most distinct and consistent trabecular bone morphology, with dense trabeculae indicative of the ability to withstand forces in a variety of directions. Scutisorex exhibits an additional external morphology region compared to unmodified shrews, but this region does not correspond to a change in trabecular architecture. Although trabecular bone architecture is regionalized along the soricid vertebral column, and this regionalization is potentially related to bone functional adaptation, there are likely aspects of vertebral functional regionalization that are not detectable using trabecular bone morphology. For example, the external morphology of the Scutisorex lumbar spine shows signs of an extra functional region that is not apparent in trabecular bone analyses. It is possible that body size and locomotor mode affect the degree to which function is manifest in trabecular bone, and broader study across mammalian size and ecology is warranted to understand the relationship between trabecular bone morphology and other measures of vertebral function such as intervertebral range of motion.

High-resolution CT Image Retrieval Using Sparse Convolutional Neural Network

We propose a high-resolution CT image retrieval method based on sparse convolutional neural network. The proposed framework is used to train the end-to-end mapping from low-resolution to high-resolution images. The patch-wise feature of low-resolution CT is extracted and sparsely represented by a convolutional layer and a learned iterative shrinkage threshold framework, respectively. Restricted linear unit is utilized to non-linearly map the low-resolution sparse coefficients to the high-resolution ones. An adaptive high-resolution dictionary is applied to construct the informative signature which is highly connected to a high-resolution patch. Finally, we feed the signature to a convolutional layer to reconstruct the predicted high-resolution patches and average these overlapping patches to generate high-resolution CT. The loss function between reconstructed images and the corresponding ground truth high-resolution images is applied to optimize the parameters of end-to-end neural network. The well-trained map is used to generate the high-resolution CT from a new low-resolution input. This technique was tested with brain and lung CT images and the image quality was assessed using the corresponding CT images. Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean absolute error (MAE) indexes were used to quantify the differences between the generated high-resolution and corresponding ground truth CT images. The experimental results showed the proposed method could enhance images resolution from low-resolution images. The proposed method has great potential in improving radiation dose calculation and delivery accuracy and decreasing CT radiation exposure of patients.

Accurate micro-computed tomography imaging of pore spaces in collagen-based scaffold

In this work we have used X-ray micro-computed tomography (μCT) as a method to observe the morphology of 3D porous pure collagen and collagen-composite scaffolds useful in tissue engineering. Two aspects of visualizations were taken into consideration: improvement of the scan and investigation of its sensitivity to the scan parameters. Due to the low material density some parts of collagen scaffolds are invisible in a μCT scan. Therefore, here we present different contrast agents, which increase the contrast of the scanned biopolymeric sample for μCT visualization. The increase of contrast of collagenous scaffolds was performed with ceramic hydroxyapatite microparticles (HAp), silver ions (Ag(+)) and silver nanoparticles (Ag-NPs). Since a relatively small change in imaging parameters (e.g. in 3D volume rendering, threshold value and μCT acquisition conditions) leads to a completely different visualized pattern, we have optimized these parameters to obtain the most realistic picture for visual and qualitative evaluation of the biopolymeric scaffold. Moreover, scaffold images were stereoscopically visualized in order to better see the 3D biopolymer composite scaffold morphology. However, the optimized visualization has some discontinuities in zoomed view, which can be problematic for further analysis of interconnected pores by commonly used numerical methods. Therefore, we applied the locally adaptive method to solve discontinuities issue. The combination of contrast agent and imaging techniques presented in this paper help us to better understand the structure and morphology of the biopolymeric scaffold that is crucial in the design of new biomaterials useful in tissue engineering.

X-ray deconvolution microscopy

Recent advances in single-photon-counting detectors are enabling the development of novel approaches to reach micrometer-scale resolution in x-ray imaging. One example of such a technology are the MEDIPIX3RX-based detectors, such as the LAMBDA which can be operated with a small pixel size in combination with real-time on-chip charge-sharing correction. This characteristic results in a close to ideal, box-like point spread function which we made use of in this study. The proposed method is based on raster-scanning the sample with sub-pixel sized steps in front of the detector. Subsequently, a deconvolution algorithm is employed to compensate for blurring introduced by the overlap of pixels with a well defined point spread function during the raster-scanning. The presented approach utilizes standard laboratory x-ray equipment while we report resolutions close to 10 μm. The achieved resolution is shown to follow the relationship [Formula: see text] with the pixel-size p of the detector and the number of raster-scanning steps n.

TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation

In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.

Self-adapting denoising, alignment and reconstruction in electron tomography in materials science

An automatic procedure for electron tomography is presented. This procedure is adapted for specimens that can be fashioned into a needle-shaped sample and has been evaluated on inorganic samples. It consists of self-adapting denoising, automatic and accurate alignment including detection and correction of tilt axis, and 3D reconstruction. We propose the exploitation of a large amount of information of an electron tomography acquisition to achieve robust and automatic mixed Poisson-Gaussian noise parameter estimation and denoising using undecimated wavelet transforms. The alignment is made by mixing three techniques, namely (i) cross-correlations between neighboring projections, (ii) common line algorithm to get a precise shift correction in the direction of the tilt axis and (iii) intermediate reconstructions to precisely determine the tilt axis and shift correction in the direction perpendicular to that axis. Mixing alignment techniques turns out to be very efficient and fast. Significant improvements are highlighted in both simulations and real data reconstructions of porous silicon in high angle annular dark field mode and agglomerated silver nanoparticles in incoherent bright field mode. 3D reconstructions obtained with minimal user-intervention present fewer artefacts and less noise, which permits easier and more reliable segmentation and quantitative analysis. After careful sample preparation and data acquisition, the denoising procedure, alignment and reconstruction can be achieved within an hour for a 3D volume of about a hundred million voxels, which is a step toward a more routine use of electron tomography.

Longitudinal in vivo microcomputed tomography of mouse lungs: No evidence for radiotoxicity

Before microcomputed tomography (micro-CT) can be exploited to its full potential for longitudinal monitoring of transgenic and experimental mouse models of lung diseases, radiotoxic side effects such as inflammation or fibrosis must be considered. We evaluated dose and potential radiotoxicity to the lungs for long-term respiratory-gated high-resolution micro-CT protocols. Free-breathing C57Bl/6 mice underwent four different retrospectively respiratory gated micro-CT imaging schedules of repeated scans during 5 or 12 wk, followed by ex vivo micro-CT and detailed histological and biochemical assessment of lung damage. Radiation exposure, dose, and absorbed dose were determined by ionization chamber, thermoluminescent dosimeter measurements and Monte Carlo calculations. Despite the relatively large radiation dose delivered per micro-CT acquisition, mice did not show any signs of radiation-induced lung damage or fibrosis when scanned weekly during 5 and up to 12 wk. Doubling the scanning frequency and once tripling the radiation dose as to mimic the instant repetition of a failed scan also stayed without detectable toxicity after 5 wk of scanning. Histological analyses confirmed the absence of radiotoxic damage to the lungs, thereby demonstrating that long-term monitoring of mouse lungs using high-resolution micro-CT is safe. This opens perspectives for longitudinal monitoring of (transgenic) mouse models of lung diseases and therapeutic response on an individual basis with high spatial and temporal resolution, without concerns for radiation toxicity that could potentially influence the readout of micro-CT-derived lung biomarkers. This work further supports the introduction of micro-CT for routine use in the preclinical pulmonary research field where postmortem histological approaches are still the gold standard.

A multiresolution approach to discrete tomography using DART

In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.