Beam Hardening Effect Research Articles

Effects of lead shielding on gamma radiation scatter energy spectrum during equine bone scintigraphy.

The main aim of this pilot study was to determine how the energy spectrum of scatter radiation emitted from horses after injection of the radiopharmaceutical 99mTechnetium-methyl diphosphonate (99mTc-MDP), changed behind lead shielding of varying thicknesses (0.25 mm, 0.35 mm, and 0.5 mm Pb thickness), and if beam hardening occurred. The effect lead shielding has on the emitted gamma radiation energy spectrum has not been documented. In particular, the presence of beam hardening effects behind lead shielding was investigated, to determine whether or not it could discourage the use of lead shields during bone scintigraphy in horses. Horses were injected intravenously with 99mTc-MDP, and energy spectra emitted from horses without lead shielding were recorded initially to determine the emitted scatter spectrum. Thereafter, different combinations of lead shields of the various thicknesses listed above, draped over the horse and on simulated personnel, were recorded. The energy spectra were obtained at different anatomical locations of five horses on five consecutive days with a pulse height (multichannel) analyser two and a half hours post-injection. Energy spectra recorded from horses without lead shielding showed polychromatic energy spectra that encompassed a large portion of predominantly lower scatter energies (averaging around the 88-94 keV peaks). Higher 99mTc- MDP peaks averaging at 139-143 keV (useful for gamma camera acquisition) were consistently seen in all recordings but made up a very small part of the emitted spectra. With the application of lead shielding, peaks of 83-86 keV, which coincided with K-edges of lead, occurred. No significant beam hardening effects behind lead shields of varying thicknesses were observed. Thus, the wearing of lead shields during bone scintigraphy of horses is encouraged.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100 μm, a thickness of 500 μm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

A 2D rapid quantitative method for wetting and drying process in cementitious materials based on X-ray radiography

The infiltration of water is a crucial factor in the degradation of the durability of concrete structures. It is important to conduct wetting and drying experiments to validate the water transport performance of construction materials during their service life, with precise measurement of both the process and the outcomes being essential. This study proposes a 2D quantitative detection method based on X-ray Radiography to determine the distribution of moisture content. This technique enhances conventional X-ray Radiography methods by utilizing a higher tube voltage to mitigate the impact of beam-hardening effects, as explained by the monochromatic index introduced here, and employs a coefficient matrix for correcting field non-uniformity and a spatial positioning algorithm to rectify discrepancies among multiple measurements. This method offers several benefits due to its capacity to provide in-time, accurate, non-contact, and cost-efficient quantitative results without any contrast agent while maintaining appropriate spatial resolution. The feasibility of this method is verified through a wetting and drying experiment taking cement paste material as an example. When compared with the gravimetric method, experimental results show that the relative error of moisture content obtained from this method is less than 5.76 %.

Statistical image reconstruction with beam-hardening compensation for X-ray CT by a calibration step (2DIterBH).

The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters. We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms. The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system. Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions. Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution.

Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique.

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70keV (VMI70) and 140keV (VMI140) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI70 and VMI140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

Semi-supervised segmentation of metal-artifact contaminated industrial CT images using improved CycleGAN.

Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research.

Correcting Hardening Artifacts of Aero-Engine Blades with an Iterative Linear Fitting Technique Framework.

Aero engines are the key power source for aerospace vehicles. Cermet turbine blades are the guarantee for the new-generation fighters to improve aero-engine overall performance. X-ray non-destructive reconstruction can obtain the internal structure and morphology of cermet turbine blades. However, the beam hardening effect causes artifacts in objects and affects the reconstruction quality, which is an issue that needs to be solved urgently. This study proposes a hardening-correction framework for industrial computed tomography (ICT) images based on iterative linear fitting. First, an iterative binarization was performed to improve the penetration length accuracy of the forward projection. Then, the proposed linear fitting technology combined with the Hermite function model is derived and analyzed to obtain suitable parameters of blade data. Finally, the fitting curves of the blade data, using the proposed method and the traditional polynomial fitting method, were analyzed and compared and were used to correct the engine turbine blade projection data to reconstruct different groups of tomographic images. Different groups of tomographic images were analyzed using three quantitative image quality evaluation indicators. The results show that the root-mean-square error (RMSE) of the tomographic image obtained by the proposed framework is 0.0133, which is lower than that of the compared method. The peak signal-to-noise ratio (PSNR) is 37.7050 dB and the feature structural similarity (FSIM) is 0.9881, which are both higher than that of the compared method. The proposed method improves the hardening-artifact-correction capability and can obtain higher-quality images, which provides new ideas for the development of imaging and detection of new-generation aero-engine turbine blades.

Beam-hardening correction method for quantifying moisture content in cement paste based on X-ray radiography

The X-ray spectrum changes while traveling through specimen, and this phenomenon, known as the beam-hardening effect, breaks the linear relationship between the intensity variety on radiographs and material thickness. In the detection of water transport in cement-based materials, this effect leads to inaccurate results of the quantitative moisture distribution inside the sample, and it is difficult to eliminate this effect by gravimetric calibration. To solve this problem, this paper proposed a calibration strategy for the attenuation coefficient curve of cement-water two-phase specimens, and provided a heuristic mathematical model for fitting the 2D attenuation coefficient surface of cement paste specimens. The experiments showed that for zones close to saturation, beam-hardening leads to an overestimation of moisture content, while for a zone with a low water content, the X-ray radiography results without correction tend to underestimate it. After correction, the method showed a quantitative result close to that of the gravimetric method, and maintained a good stability in samples of different thickness.

Abstract TP138: Image Quality Assessment of Photon Counting Computed Tomography Angiography of the Head and Neck

Introduction: Photon-counting computed tomography (PCCT) is a novel advanced CT technology that enables higher spatial resolution, spectral imaging to resolve tissue types and can reduce artifacts. Cerebrovascular image quality from head and neck CTAs acquired on a photon-counting detector was assessed. Methods: Subjects who underwent head and neck PCCTAs for suspected ischemic stroke were retrospectively identified (mean age=57.5 years, N=6 men). Images were acquired with a tube voltage of 120 kVp. Tube current was adjusted based on patient size. Image quality was assessed on a polyenergetic reconstruction. Two neuroradiologists measured mean attenuation [Hounsfield unit (HU)], image noise, and contrast-to-noise ratio (CNR)] of the head/neck arteries. Beam hardening effects on the arteries were calculated using regions of interests (ROIs). CNR was calculated as the absolute difference between the HU mean of the arterial segment and surrounding fat divided by image noise. Intraclass correlation coefficients (ICC) were calculated to measure reliability. Results: Ten head and neck PCCTAs met criteria for diagnostic quality (100% interrater agreement). The CNR of all arterial segments of the extra- and intracranial arteries ranged from 40.7 to 52.7. Effects of beam hardening were worse at the vertebral artery origins, internal carotid artery (ICA)-C3 (cavernous) and ICA-C4 (clinoid) segments. Artifact could be reduced using higher virtual monoenergetic levels, an advantage for segments vulnerable to arterial dissections, such as the vertebral artery origins. Interreader agreement of HU ROIs was ICC&gt;0.7 for each arterial segment. Conclusions: Vertebral artery origins near the arch in the neck and cavernous/clinoid ICA segments in the head showed the greatest need for optimization to reduce artifacts and image noise. Future work will focus on leveraging PCCT technology to select the optimal energy level, reduce artifacts and improve diagnostic accuracy.

Metallic artifacts-free spectral computed tomography angiography based on renal clearable bismuth chelate

Computed tomography angiography (CTA) is one of the most important diagnosis techniques for various vascular diseases in clinic. However, metallic artifacts caused by metal implants and calcified plaques in more and more patients severely hinder its wide applications. Herein, we propose an improved metallic artifacts-free spectral CTA technique based on renal clearable bismuth chelate (Bi-DTPA dimeglumine) for the first time. Bi-DTPA dimeglumine owns the merits of ultra-simple synthetic process, approximately 100% of yield, large-scale production capability, good biocompatibility, and favorable renal clearable ability. More importantly, Bi-DTPA dimeglumine shows superior contrast-enhanced effect in CTA compared with clinical iohexol at a wide range of X-ray energies especially in higher X-ray energy. In rabbits’ model with metallic transplants, Bi-DTPA dimeglumine assisted-spectral CTA can not only effectively mitigate metallic artifacts by reducing beam hardening effect under high X-ray energy, but also enables accurate delineation of vascular structure. Our proposed strategy opens a revolutionary way to solve the bottleneck problem of metallic artifacts in CTA examinations.

GPU-accelerated polyenergetic forward projection for 9 MeV industrial CT system

Polyenergetic forward projection has great significance in inspecting hazardous materials, establishing optimal radiographic variables and investigating beam hardening effects. However, it is computationally intensive to perform polyenergetic forward-projection calculations for high-resolution phantoms. To address this issue, a rapid polyenergetic forward-projection algorithm is proposed for a 9 MeV industrial computed tomography (CT) system. The FLUktuierende KAskade (FLUKA) software package is used to generate the 9 MeV X-ray spectrum data. Two voxelised phantoms are used to model scanned objects, one being a multi-material cylinder and the other a single-material turbine blade. An incremental version of Siddon's algorithm is adopted to calculate the intersection lengths between the X-rays and the auxiliary phantoms. Three strategies are utilised to accelerate the calculation, in which: the intersection lengths do not vary with the energy bins and can be used repeatedly until all the energy bins are counted; a graphics processing unit (GPU) is used to accelerate the ray tracing algorithm by utilising a parallel computing technique; and faster memory access is achieved by binding the auxiliary phantoms to texture objects. The simulation results in this paper show that the GPU-based approach not only maintains the image precision but also gains significant speed-ups over the conventional central processing unit (CPU)-based Siddon method. Furthermore, beam hardening artefacts can clearly be seen from the profile curves of the reconstructed slices, indicating that this method is effective.

Pixel-wise beam-hardening correction for dark-field signal in X-ray dual-phase grating interferometry.

The dark-field signal provided by X-ray grating interferometry is an invaluable tool for providing structural information beyond the direct spatial resolution and their variations on a macroscopic scale. However, when using a polychromatic source, the beam-hardening effect in the dark-field signal makes the quantitative sub-resolution structural information inaccessible. Especially, the beam-hardening effect in dual-phase grating interferometry varies with spatial location, inter-grating distance, and diffraction order. In this work, we propose a beam-hardening correction algorithm, taking into account all these factors. The accuracy and robustness of the algorithm are then validated by experimental results. This work contributes a necessary step toward accessing small-angle scattering structural information in dual-phase grating interferometry.

Mineral quantitative characterization method based on basis material decomposition model by dual-energy computed tomography.

Dual-energy computed tomography (DECT) can reconstruct electron density ρe and effective atomic number Zeff distribution for material discrimination. Image-domain basis material decomposition (IBMD) method is a widely used DECT method. However, IBMD method cannot be used for mineral identification directly due to limitations of complex basis material determination, beam hardening artifacts, and inherent errors caused by approximate empirical formulas. This study proposes an improved IBMD (IIBMD) method to overcome the above limitations. In IIBMD method, the composition of basis material is optimized to obtain accurate decomposition coefficients, which enables accurate ρe and Zeff distribution. Moreover, the thickness of basis material is optimized to reduce the effect of beam hardening. Furthermore, two formulas in place of empirical formulas are proposed to calculate ρe and Zeff. Finally, a threshold technique is applied to separate different mineral phases. Numerical simulations and practical experiments using a photon-counting detector CT system are implemented to verify IIBMD method. Results show that the relative errors of ρe and Zeff for seven common minerals are down to 5%, lower than most of the existing DECT methods for rocks. Reasonable volume fraction results of mineral phases are thus obtained through threshold segmentation. This study demonstrates that the proposed IIBMD method has high practical value in mineralogical identification.

Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography.

In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20-160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values.

Accurate Image Reconstruction in Dual-Energy CT with Limited-Angular-Range Data Using a Two-Step Method.

Dual-energy CT (DECT) with scans over limited-angular ranges (LARs) may allow reductions in scan time and radiation dose and avoidance of possible collision between the moving parts of a scanner and the imaged object. The beam-hardening (BH) and LAR effects are two sources of image artifacts in DECT with LAR data. In this work, we investigate a two-step method to correct for both BH and LAR artifacts in order to yield accurate image reconstruction in DECT with LAR data. From low- and high-kVp LAR data in DECT, we first use a data-domain decomposition (DDD) algorithm to obtain LAR basis data with the non-linear BH effect corrected for. We then develop and tailor a directional-total-variation (DTV) algorithm to reconstruct from the LAR basis data obtained basis images with the LAR effect compensated for. Finally, using the basis images reconstructed, we create virtual monochromatic images (VMIs), and estimate physical quantities such as iodine concentrations and effective atomic numbers within the object imaged. We conduct numerical studies using two digital phantoms of different complexity levels and types of structures. LAR data of low- and high-kVp are generated from the phantoms over both single-arc (SA) and two-orthogonal-arc (TOA) LARs ranging from 14∘ to 180∘. Visual inspection and quantitative assessment of VMIs obtained reveal that the two-step method proposed can yield VMIs in which both BH and LAR artifacts are reduced, and estimation accuracy of physical quantities is improved. In addition, concerning SA and TOA scans with the same total LAR, the latter is shown to yield more accurate images and physical quantity estimations than the former. We investigate a two-step method that combines the DDD and DTV algorithms to correct for both BH and LAR artifacts in image reconstruction, yielding accurate VMIs and estimations of physical quantities, from low- and high-kVp LAR data in DECT. The results and knowledge acquired in the work on accurate image reconstruction in LAR DECT may give rise to further understanding and insights into the practical design of LAR scan configurations and reconstruction procedures for DECT applications.

Comparison of conventional and through glass portable chest computed radiography: A Phantom study

Background: Since the outbreak of COVID-19, modified hospital unit or area for chest radiography of positive cases have become necessary. To date, relatively few studies have been investigated on the effects of portable chest radiography through glass barrier. Objectives: Our goal was to evaluate exposure technique and radiation dose between conventional and through glass portable chest computed radiography. Materials and methods: Experiments using an anthropomorphic phantom were performed for acquired portable chest PA radiography at SID 180 cm with glass door being open and closed. The EI and DI values were optimized to provide the appropriate exposure technique for glass barrier. Entrance surface air kerma and scatter survey were made to assess the radiation dose both inside and outside the room. Finally, HVL measurement of primary X-ray beam and after transmission through glass were determined. Results: Based on the fixed kVp and mAs technique, the EI value with glass barrier was less than the EI without the glass. Imaging through glass barrier showed the average EI reduction of 10.4% for Carestream and 37% for Konica. The average entrance surface air kerma reduction was 56.6% over a range of 90-120 kVp. The appropriate exposure technique for conventional portable chest PA using computed radiography was 100 kVp 2.5 mAs. With the same kVp setting, doubling the mAs is required for imaging through glass barrier to produce good diagnostic image quality (100 kVp and 4.0 mAs). The acceptable EI and DI ranges for CR used were EI=1742, DI=-0.02 (without glass) and EI=1795, DI=0.11 (with glass) for Carestream and EI=352, DI=-0.03 (without glass) and EI=373, DI=0.22 (with glass) for Konica respectively. The primary beam after transmission through the glass thickness 5 mm was 36%. The measured scatter of inside room compared to outside was very low at 1-2 meters. Increasing od HVL from 3.9 to 6.1 mm Al indicates the effect of beam hardening by glass. Conclusion: These experiments confirmed that through glass portable chest computed radiography are feasible and safe. The findings of this study have several practical implications which minimizes risk to radiographers during their work.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction.

The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations. cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV ). Images were also compared to a nonspectral TV reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitativelyevaluated. Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV algorithm and -154 HU error for the nonspectral TV +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV algorithm, 41 HU for the MLE+TV +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value wasselected. By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed byreconstruction.

Novel U-net based deep neural networks for transmission tomography.

The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images. In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise. In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account. This strong connection enables us to enhance the projection data and the reconstructed image together. We tested the two proposed models against three other methods on two datasets. The datasets contain physically correct simulated data, and they show strong signs of beam hardening and electrical noise. We also performed a numerical evaluation of the neural networks on the reconstructed images according to three error measurements and provided a scoring system of the methods derived from the three measures. The results showed the superiority of the novel architecture called TomoNet2. TomoNet2 improved the quality of the images according to the average Structural Similarity Index from 0.9372 to 0.9977 and 0.9519 to 0.9886 on the two data sets, when compared to the FBP method. This network also yielded the best results for 79.2 and 53.0 percent for the two datasets according to Peak-Signal-to-Noise-Ratio compared to the other improvement techniques. Our experimental results showed that the reconstruction step used in skip connections in deep neural networks improves the quality of the reconstructions. We are confident that our proposed method can be effectively applied to other datasets for tomographic purposes.

Experimental proof of moisture clog through neutron tomography in a porous medium under truly one‐directional drying

AbstractStructural failure of concrete buildings on fire and complete destruction of the monolithic refractory lining during their drying stage are dangerous examples of the effect of explosive spalling on partially saturated porous media. Several observations in both cases indicated the presence of moisture accumulation ahead of the drying front, which are in tune with the most common theories on the explosive spalling of concrete. Previous studies have shown evidence of the existence of this phenomenon, however, they were biased by artifacts and experimental limitations (such as the beam hardening effect and changes in the microstructure of the material due to the presence of pressure and temperature sensors). In the current work, rapid neutron tomography was used to investigate the in‐operando drying behavior of a high‐alumina refractory castable, proposing a novel experimental layout aimed at a truly one‐dimensional drying front. This setup provided more realistic boundary conditions, such as the behavior of a larger wall heated from one of its sides, while also preventing some nonphysical artifacts (notably beam hardening). By eliminating these aspects, a direct proof that moisture accumulates ahead of the drying front was obtained. This work also lays the basis for further studies focusing on the response sensitivity analysis to boundary conditions and other parameters (e.g., heating rates and properties of the sample related to the moisture clog formation), as well as useful data for the validation and characterization stages of numerical models of partially saturated porous media.

A Deep Recurrent Neural Network With FISTA Optimization for CT Metal Artifact Reduction

Metal Artifact Reduction (MAR) is one of the most challenging problems in Computed Tomography (CT) imaging. In CT imaging, metal implants in patient's bodies cause artifacts due to several factors, such as beam hardening effects, statistical property changes of the X-ray beams, and the shapes of metal implants. Although some promising results have been achieved by previously proposed model-based iterative reconstruction (IR) techniques, there is still much room for improvement. One of the problems is that the image prior models used in the IR techniques are too simple to capture the truly complex nature of the CT images. Recent advances in neural network deep learning (DL) can help address this problem and potentially improve MAR results significantly. In this work, we describe a novel DL-based technique for CT MAR. In this technique, we introduce a novel deep neural network based on an IR formulation and a convex optimization technique known as the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm). The neural network, called the RNN-MAR, is an RNN (Recurrent Neural Network) composed of a set of proposed RFUs (Recurrent FISTA Units). While the structure of the RFU has some connections to the GRU (Gated Recurrent Unit), it is specifically designed for CT MAR. The RNN-MAR conducts dual-domain learning (image and sinogram) but can do this using only one objective function. Furthermore, unlike previous CT MAR techniques, the RNN-MAR does not use a binary metal trace. Instead, we used a novel real-valued sinogram domain confidence map, leading to smoother edges. Results from extensive experiments indicate that our RNN-MAR outperforms state-of-the-art DL MAR techniques in terms of the Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity (SSIM) as well as in terms of visual appearance.