Multi-energy X-ray Research Articles

Radiated power and soft x-ray diagnostics in the SMART tokamak.

A multi-energy soft x-ray diagnostic is planned to operate in the small aspect ratio tokamak (SMART), consisting of five cameras: one for core measurements, two for edge, and two for divertors. Each camera is equipped with four absolute extreme ultra-violet diodes, with three of them filtered by Ti and Al foils for C and O line emissions, respectively, and Be foils for temperature measurements. In addition, two spectrometers will be installed with a vertical line of sight for impurity control. This study introduces a synthetic model designed to characterize radiated power and soft x-ray emissions. The developed code extracts the radiated power and Zeff values by leveraging distributions of electron density, temperatures, and impurity concentrations. The investigation is centered on the predicted scenarios of SMART's first phase of operation (Ip = 100kA; Bt = 0.1T), employing a double-null configuration with positive and negative triangularity. The anticipated impurities encompass C (1%) and Fe (0.01%) from the vessel, as well as O and N (0.1%) from air and water. For simplicity, the distribution is assumed to be homogeneous within the plasma, considering different mixtures with Zeff values ranging between 1 and 2. Finally, the model estimates signal strength for the diagnostic design, proving its feasibility.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic and ex-vivo phantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

Versatile multi-energy hard x-ray camera to study confined and unconfined fast electron dynamics and anisotropies (Invited).

A powerful and flexible hard x-ray (HXR) camera has been recently installed and tested on the WEST tokamak (CEA, France) in collaboration with the Princeton Plasma Physics Laboratory. The diagnostic is a pinhole camera fielded with a 2D pixel detector equipped with a 1mm thick CdTe sensor. The novelty of this diagnostic technique is the detector's capability of adjusting the threshold energy at the pixel level. This innovation provides great flexibility in the energy configuration, allowing simultaneous space, energy, and time resolved x-ray measurements. The novel camera has been used to measure the core radiation from non-Maxwellian (fast) electrons accelerated by Lower Hybrid (LH) waves and also the beam-target emission of tungsten in the divertor region produced by fast electron losses interacting with the target. In addition, anisotropic hard x-ray emission has been detected for the first time at the WEST core and edge plasma, with opposite toroidal intensity trends. Experimental vertical and toroidal HXR profiles have been successfully reproduced with the LH code LUKE.

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

Design of a multi-energy soft X-ray diagnostic for profile measurements during long-pulse operation in the WEST tokamak

The W Environment in Steady-state Tokamakwas designed and built to test ITER-like tungsten plasma facing components in a long pulse (∼1000 s) scenario. Recently, a multi-energy soft x-ray diagnostic (MESXR) was installed in the WEST (W Environment in Steady-state Tokamak), to understand the sources, transport and confinement of high-Z impurities. The purpose of this work is to describe the engineering challenges posed by the long pulse scenario for the integration of the MESXR diagnostic and how they were addressed and solved. The calculations of vacuum and thermal stress as well as heat transfer are presented and discussed.

Determination of the mean energy of fast electron losses and anisotropies through thick-target emission on WEST

A new method to obtain the mean energy of fast electron losses in fusion plasmas using a versatile multi-energy hard x-ray (HXR) detector is presented. The method is based on measuring the thick-target emission of tungsten in the divertor region produced by fast electron losses interacting with the target and modeling the tungsten spectra by a Monte Carlo code which simulates the interaction between a beam of electrons and a solid target. The mean energy of the fast electron losses is determined through the comparison between the experimental and synthetic emission. The results show that fast electron losses during lower hybrid current drive discharges at WEST have a mean energy of 90–140 keV and represent only 2% of the total heat flux at the target. Additionally, anisotropic HXR emission has been detected for the first time at the WEST core and edge plasma, with opposite directions. It is due to the forward-peak emission of two distinctive populations of fast electrons: co-current fast electrons in the core and counter-current fast electron losses at the inner strike point. In view of future experiments like ITER where electron cyclotron current drive will generate a fast electron population, this technique could serve as a real-time monitor of fast electron losses and eventually feed an actuator on the current drive generation.

Highlighting the effects of gamma irradiation of the brain through non-conventional X-ray imaging

Whole brain irradiation is largely used as an alternative radiotherapy of brain tumors that cannot be eliminated by surgery so the effects of ionizing radiation on brain healthy tissue represents an important research domain. The aim of this study is to evaluate the irradiation effects on in vitro brain tissue spheroids models as well as the whole mouse brain using standard cellular biology assays and a multi-energy X-ray imaging technique. The spheroids irradiated with gamma rays (dose between 0–30 Gy) and treated with biocompatible nanoparticles consisting of concentrations between 0–100 μM proved to be morphologically stable and with a high radio-resistance. The reactive oxygen species concentration and the γ-H2AX foci number increase with the irradiation dose, as expected. The X-ray imaging with dual-energy technique method proposed here was able to differentiate between irradiated and control samples (whole brain). Concluding, our results proved the expected effects of ionizing radiation on brain tissue. The dual-energy X-ray imaging method tested here appears as a promising method for characterizing the ionizing radiation effects on the whole brain level.

Optimization of S2-alar-iliac screw (S2AI) fixation in adult spine deformity using a comprehensive genetic algorithm and finite element model personalized to patient geometry and bone mechanical properties.

To optimize the biomechanical performance of S2AI screw fixation using a genetic algorithm (GA) and patient-specific finite element analysis integrating bone mechanical properties. Patient-specific pelvic finite element models (FEM), including one normal and one osteoporotic model, were created from bi-planar multi-energy X-rays (BMEXs). The genetic algorithm (GA) optimized screw parameters based on bone mass quality (BM method) while a comparative optimization method maximized the screw corridor radius (GEO method). Biomechanical performance was evaluated through simulations, comparing both methods using pullout and toggle tests. The optimal screw trajectory using the BM method was more lateral and caudal with insertion angles ranging from 49° to 66° (sagittal plane) and 29° to 35° (transverse plane). In comparison, the GEO method had ranges of 44° to 54° and 24° to 30° respectively. Pullout forces (PF) using the BM method ranged from 5 to 18.4kN, which were 2.4 times higher than the GEO method (2.1-7.7kN). Toggle loading generated failure forces between 0.8 and 10.1kN (BM method) and 0.9-2.9kN (GEO method). The bone mass surrounding the screw representing the fitness score and PF of the osteoporotic case were correlated (R2 > 0.8). Our study proposed a patient-specific FEM to optimize the S2AI screw size and trajectory using a robust BM approach with GA. This approach considers surgical constraints and consistently improves fixation performance.

Inner product regularized multi-energy X-ray tomography for material decomposition

Inner product regularized multi-energy X-ray tomography for material decomposition

The Application of Dual-layer Spectral Detector CT in Abdominal Vascular Imaging.

As a convenient and non-invasive diagnostic method, computed tomography (CT) has been developing continuously, and dual-energy CT imaging is one of its current research hotspots. Dualenergy CT, using two different X-ray energies for imaging, can generate spectral image sets such as virtual monoenergetic images, virtual non-contrast images, iodine density images, uric acid images, calcium inhibition images, and effective atomic number images. These images could help to increase the contrast of vascular, improve the detection rate of lesions, reduce artifacts, reduce the dose of radiation, and characterize materials. Dual-layer spectral detector CT, a detector-based dual-energy scanning device, has an X-ray tube and a dual-layer X-ray detector that can simultaneously separate lowenergy and high-energy photons from a multi-energy X-ray beam, which means excellent time registration. This paper aims to introduce the applications of dual-layer spectral detector CT in abdominal angiography, including optimizing image quality, reducing the dose of contrast agent and radiation, providing richer diagnostic information, organ perfusion, and thrombus identification.

A novel method for assigning bone material properties to a comprehensive patient-specific pelvic finite element model using biplanar multi-energy radiographs

The increasing prevalence of adult spinal deformity requires long spino-pelvic instrumentation, but pelvic fixation faces challenges due to distal forces and reduced bone quality. Bi-planar multi-energy X-rays (BMEX) were used to develop a patient-specific finite element model (FEM) for evaluating pelvic fixation. Calibration involved 10 patients, and an 81-year-old female test case was used for FEM customization and pullout simulation validation. Calibration yielded a root mean square error of 74.7 mg/cm3 for HU. The simulation accurately replicated the experimental pullout test with a force of 565 N, highlighting the method’s potential for optimizing biomechanical performance for pelvic fixation.

Tangential extreme ultraviolet and soft x-ray diagnostic system for time-resolved temperature measurement on the High Beta Tokamak-Extended Pulse.

The High Beta Tokamak-Extended Pulse has recently incorporated a tangential multi-energy extreme ultraviolet and soft x-ray diagnostic system. This system enables measurements of the electron temperature and the examination of mode dynamics within the tokamak. While other systems have been built for poloidal views over similar temperature ranges, this is the first multi-energy tangential-view system designed to work in a temperature range below 200eV in a tokamak. To facilitate these measurements, a filter wheel comprising five distinct groups of dual-filters has been developed and implemented. By employing a combination of 0.1μm aluminum and 0.2μm titanium filters, the system allows estimation of electron temperature profiles through reconstruction of the emission profile using the standard "double-foil" technique. The influence of impurities and filter oxide layers on measurement outcomes is examined. Results reveal that, while the absolute electron temperature values may exhibit some deviations, key characteristics like the electron temperature profile shape and inversion radius during sawtooth events remain consistent. This consistency confirms the system's suitability for core plasma studies. This system has proven effective in detecting and analyzing internal magnetohydrodynamic phenomena, such as sawteeth.

Identification of an Unknown Substance by the Methods of Multi-Energy Pulse X-ray Tomography

The inverse problem for the non-stationary radiative transfer equation is considered, which consists in finding the attenuation coefficient according to the pulsed multi-energy X-ray exposure. For a short duration of the probing pulse, the asymptotic solution of the inverse problem is found. The problem of identifying an unknown substance by attenuation coefficients approximately found on a finite set of energy values is formulated. Algorithms for solving identification problems are proposed. The results of the numerical simulation are presented for a wide range of substances of interest in medical computed tomography.

Precise nutritional labelling of sliced packaged dry-cured ham using multi-energy X-ray absorptiometry

New consumer demands have increased the need for improved food processing and new value-added products that meet the latest quality standards. Changes in eating habits may lead to a preference for lower sodium products, making accurate labelling and nutritional claims important for the industry. The aim of this work was to study the application of Multi-energy X-ray absorptiometry (MEXA) for the determination of nutritional information in sliced packaged dry-cured ham for the industry. The effect of the acquisition conditions, the analysis approach, fat content and measured area of slices, as well as the potential of this technology for the inclusion of a verified ‘salt reduced’ nutritional claim, were analyzed in two industrial case studies. Two hundred and ten packets of sliced dry-cured ham were scanned using MEXA equipment. Two regions of interest were selected to study the effect of thickness on the model's precision. Salt content could be predicted with a RMSEP of 0.346% and 0.403% when acquisition conditions were 80 keV and 110 keV respectively. When used by the industry, the classification performance for a ‘salt reduced’ labelling claim depends on the mean salt content and heterogeneity of the company's production and on the threshold value selected for class definition. However, to support consumers personalized nutrition through precise labelling, implementation of MEXA technology together with labelling system is necessary.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data.

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times. Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

Urban mining of unexploited spent critical metals from E-waste made possible using advanced sorting

The growing number of electronic devices has led to a surge in e-waste, making efficient recycling essential to reduce environmental impact and recover valuable metals. However, traditional recycling methods struggle to extract them due to their low concentrations in e-waste. Here, we developed a system to sort electronic components from printed circuit boards by elemental composition. It combines a convolutional neural network-based optical recognition with multi-energy X-ray transmission spectroscopy, demonstrating up to 96.9% accuracy in controlled conditions. Hence, with elemental enrichments by up to 10,000 for targeted elements, this method renders economically viable the recovery of previously unrecycled critical metals by enriching sorting bags in precious, semi-precious, refractory (Ta, Nb), transition (Co, Cr, Mn, Ni, Zn, Ga, Bi, etc.) or other (In, Sn, Sb) metals. These findings demonstrate the promising applications of this technology in mitigating the environmental impact of e-waste and promoting the sustainable recovery of valuable metals.

Printable Perovskite Diodes for Broad-Spectrum Multienergy X-Ray Detection.

Multienergy X-ray detection is critical to effectively differentiate materials in a variety of diagnostic radiology and nondestructive testing applications. Silicon and selenium X-ray detectors are the most common for multienergy detection; however, these present poor energy discrimination across the broad X-ray spectrum and exhibit limited spatial resolution due to the high thicknesses required for radiation attenuation. Here, an X-ray detector based on solution-processed thin-film metal halide perovskite that overcomes these challenges is introduced. By harnessing an optimized n-i-p diode configuration, operation is achieved across a broad range of soft and hard X-ray energies stemming from 0.1 to 10's of keV. Through detailed experimental and simulation work, it is shown that optimized Cs0.1 FA0.9 PbI3 perovskites effectively attenuate soft and hard X-rays, while also possessing excellent electrical properties to result in X-ray detectors with high sensitivity factors that exceed 5 × 103 and 6 × 104 µC Gy-1 cm-2 within soft and hard X-ray regimes, respectively. Harnessing the solution-processable nature of the perovskites, roll-to-roll printable X-ray detectors on flexible substrates are also demonstrated.

Design of a five-layers multi-energy X-ray imaging detector for material sorting

X-ray transmission imaging (XRT) is widely used for sorting materials. However, conventional single-energy and dual-energy X-ray systems have poor ability to discriminate between materials with similar atomic number (Z), and the count rate of available multi-energy XRT detectors could not support high-speed industrial applications. This paper presents the design of a detector that can potentiality achieve high-speed multi-energy X-ray imaging using Geant4 simulations. This detector consisted of five detection layers (with three scintillator materials: CsI, GOS and CdWO4), two metal filters, which allows X-ray imaging at five energies. Validation simulation showed that the 15% more accurate than a dual-layers detector in the classification of Mg and Al alloys.

Non-linear fusion of dual-energy projections for XCT imaging of challenging components

The imaging of multi-material components having large changes in attenuation behaviour is widely regarded as a limitation of X-ray computed tomographic techniques. The use of Dual Energy X-ray Computed Tomography (DECT) is common in the field of medical imaging, providing many benefits to imaging and characterisation. However, multienergy methods such as DECT remain underutilised in the industrial sector. In this work, a novel measurement based multi-energy image fusion approach was evaluated using two radiographic image stacks acquired under two distinct polychromatic spectra; this fusion was then compared to the results of the individual high and low-energy datasets as well as their average. The implemented approach was tested using a Titanium (Ti) and Carbon Fibre reinforced Polymer (CFRP) double-step lap joint to evaluate performance. Results indicated improvements to contrast in Dual Energy fused radiographs, aiding the interrogation of complex components in 2D. The translation of these benefits into the Reconstructed CT volume showed promise for isolated material regions. It was established that careful parameter selection was needed to achieve optimum fusion results, as it was found that failure to do so, resulted in contrast suppression in regions of mixed materials. The process proposed here acts as a first foundational step into the fusion of multi-energy industrial X-ray computed tomography (XCT) datasets using standard XCT equipment. Further work will aim at refining and comparing it to other projection weighting functions and measures.

Inverse Method for Noninvasive Material Discrimination with Multienergy X-Ray Radiography

Active x-ray inspections have great potential for noninvasive detection of illicit materials. Dual-energy x-ray radiography, in particular, has been used to determine material composition by utilizing the energy-dependence of the x-ray attenuation coefficients. However, current implementations of this method are limited in their ability to determine the material composition of composite objects. Here we present an inverse algorithm that uses multienergy x-ray radiography data to noninvasively reconstruct the contents of a container. A critical feature of the current contribution is that material identification can be performed using conventional detectors and x-ray spectra with different endpoint energies produced by varying nothing more than the tube voltage. Adaptive regularization is used to increase the accuracy of material estimations from multienergy data sets. The utility of these methods is demonstrated with experimentally acquired radiographs obtained using a tunable x-ray source that produces spectra with endpoint energies of 100--450 keV. The object inspected is a scale model of a nuclear materials storage container composed of three-dimensional printed plastic and stainless-steel spheres inside a thin-walled steel container. Reconstructions of the steel sphere thicknesses are within a root-mean-square error of 0.37 cm.