High-energy Images Research Articles

Local Maximum Second Order Multi-synchrosqueezing Transform for High Resolution Surface-wave Group Velocity Dispersion Energy Imaging

Local Maximum Second Order Multi-synchrosqueezing Transform for High Resolution Surface-wave Group Velocity Dispersion Energy Imaging

A practical method for assessing quantitative scanner accuracy with long-lived radionuclides: The ARTnet insert.

To address the problem of using large volumes of long-lived radionuclides in test phantoms to check calibration accuracy of PET and SPECT systems we have developed a test object which (a) contains less radioactivity, (b) has a low total volume, and (c) is easier to store than currently used phantoms, while still making use of readily-available "standardised" test objects. We have designed a hollow acrylic cylindrical insert compatible with the NEMA/IEC PET Body Image Quality (IQ) phantom used in NU 2 performance testing of PET systems. The insert measures 90 mm internal diameter and 70 mm internal height and so is sufficiently large to not be subject to partial volume effects in PET or SPECT imaging. The volume of the insert is approximately 500 mL. It has been designed as a replacement for the standard long cylindrical "lung insert" in the IQ phantom without needing to remove the fillable hollow spheres of the phantom. The insert been tested with 18F, 68Ga and 124I PET/CT and 99mTc, 131I and 177Lu SPECT/CT on scanners that had previously been calibrated for these radionuclides. The scanners were found to produce accurate image reconstructions in the insert with 5% of the true value without any confounding uncertainty from partial volume effects when compared to NEMA NU 2-2018 Phantom measurement. The "ARTnet Insert" is simple to use, inexpensive, compatible with current phantoms and is suitable for both PET and SPECT systems. It does not suffer from significant partial volume losses permitting its use even with the poor spatial resolution of high-energy imaging with 131I SPECT. Furthermore, it uses less radioactivity in a smaller volume than would be required to fill the entire phantom as is usually done. Long-term storage is practical while allowing radioactive decay of the insert contents.

Utility of Photon Counting CT in Transcatheter Structural Heart Disease Interventions

Photon counting Computed Tomography (PCCT) is an innovative technology that utilizes a semiconductor detector and assigns x-ray photons to specific energy bins. PCCT features include ultra high-resolution, multi-energy binning, higher signal of iodine, lower artifacts, lower noise, and geometric radiation dose efficiency. These advantages may be highly beneficial in pre- and post-procedural assessment of transcatheter structural heart disease (SHD) interventions. Ultra-high resolution improves visualization of device structure and subtle findings such as hypoattenuated leaflet thickening, small vegetations and paravalvular leaks. High resolution also improves the evaluation of heavily calcified and stented coronary arteries, which are prevalent in high-risk transcatheter aortic valve replacement (TAVR) population. Visualization of heavily calcified coronary arteries and access vessels can also be improved by using multienergy calcium separation or high-energy virtual monoenergetic images (VMIs). PCCT allows use of low dose of iodinated contrast in severe renal dysfunction, due to inherent higher contrast-to-noise ratio of iodine, availability of a high-pitch helical mode and low-energy VMIs. High-energy VMIs can decrease artifacts including calcium blooming, beam hardening and metallic artifacts. Iodine maps enable quantification of extracellular volume without need for a non-contrast scan, facilitating the identification of cardiomyopathies like cardiac amyloidosis. Moreover, calcium score can be obtained from virtual non contrast (VNC) images derived from CT angiographic images. Additionally, VNC and iodine maps can characterize incidentally encountered lesions such as thrombus. In this article, we review the utility of PCCT in transcatheter SHD interventions.

Comparison of Photon-counting Detector and Energy-integrating Detector CT for Visual Estimation of Coronary Percent Luminal Stenosis.

Background Compared with energy-integrating detector (EID) CT, the improved resolution of photon-counting detector (PCD) CT coupled with high-energy virtual monoenergetic images (VMIs) has been shown to decrease calcium blooming on images in phantoms and cadaveric specimens. Purpose To determine the impact of dual-source PCD CT on visual and quantitative estimation of percent diameter luminal stenosis compared with dual-source EID CT in patients. Materials and Methods This prospective study recruited consecutive adult patients from an outpatient facility between January and March 2022. Study participants underwent clinical dual-source EID coronary CT angiography followed by a research dual-source PCD CT examination. For PCD CT, multienergy data were used to create VMIs at 50 and 100 keV. Two readers independently reviewed EID CT images followed by PCD CT images after a washout period. Readers visually graded the most severe stenosis in terms of percent diameter luminal stenosis for the left main, left anterior descending, right, and circumflex coronary arteries, unblinded to scanner type. Quantitative measures of percent stenosis were made using commercial software. Visual and quantitative estimates of percent stenosis were compared between EID CT and PCD CT using the Wilcoxon signed-rank test. Results A total of 25 participants (median age, 59 years [range, 18-78 years]; 16 male participants) were enrolled. On EID CT images, readers 1 and 2 identified 39 and 32 luminal stenoses, respectively, with a percent diameter luminal stenosis greater than 0%. Visual estimates of percent stenosis were lower on PCD CT images than EID CT images (reader 1: median 20.6% [IQR, 8.8%-61.2%] vs 31.8% [IQR, 12.9%-69.7%], P < .001; reader 2: 6.5% [IQR, 0.4%-54.1%] vs 22.9% [IQR, 1.8%-67.4%], P = .002). No difference was observed between EID CT and PCD CT for quantitative measures of percent stenosis (median difference, -1.5% [95% CI: -3.0%, 2.5%]; P = .51). Conclusion Relative to using EID CT, using PCD CT led to decreases in visual estimates of percent stenosis. © RSNA, 2023 See also the editorial by Murphy and Donnelly in this issue.

Investigation of test methods for QC in dual-energy based contrast-enhanced digital mammography systems: II. Artefacts/uniformity, exposure time and phantom-based dosimetry

Part II of this study describes constancy tests for artefacts and image uniformity, exposure time, and phantom-based dosimetry; these are applied to four mammography systems equipped with contrast enhanced mammography (CEM) capability. Artefacts were tested using a breast phantom that simulated breast shape and thickness change at the breast edge. Image uniformity was assessed using rectangular poly(methyl)methacrylate PMMA plates at phantom thicknesses of 20, 40 and 60 mm, for the low energy (LE), high energy (HE) images and the recombined CEM image. Uniformity of signal and of the signal to noise ratio was quantified. To estimate CEM exposure times, breast simulating blocks were imaged in automatic exposure mode. The resulting x-ray technique factors were then set manually and exposure time for LE and HE images and total CEM acquisition time was measured with a multimeter. Mean glandular dose (MGD) was assessed as a function of simulated breast thickness using three different phantom compositions: (i) glandular and adipose breast tissue simulating blocks combined to give glandularity values that were typical of those in a screening population, as thickness was changed (ii) PMMA sheets combined with polyethylene blocks (iii) PMMA sheets with spacers. Image uniformity was superior for LE compared to HE images. Two systems did not generate recombined images for the uniformity test when the detector was fully covered. Acquisition time for a CEM image pair for a 60 mm thick breast equivalent phantom ranged from 3.4 to 10.3 s. Phantom composition did not have a strong influence on MGD, with differences generally smaller than 10%. MGD for the HE images was lower than for the LE images, by a factor of between 1.3 and 4.0, depending on system and simulated breast thickness. When combined with the iodine signal assessment in part I, these tests provide a comprehensive assessment of CEM system imaging performance.

High energy x-ray Talbot-Lau interferometer employing a microarray anode-structured target source to extend the field of view.

High energy and large field of view (FOV) phase contrast imaging is crucial for biological and even medical applications. Although some works have devoted to achiving a large field of view at high energy through bending gratings and so on, which would be extremely challenging in medical high energy imaging. We analyze the angular shadowing effect of planar gratings in high-energy X-ray Talbot-Lau interferometer (XTLI). Then we design and develop an inverse XTLI coupled with a microarray anode-structured target source, to extend the FOV at high energy. Our experimental results demonstrate the benefit of the source in inverse XTLI and a large FOV of 106.6 mm in the horizontal direction is achieved at 40 keV. Based on this system, experiments of a mouse demonstrate the potential advantage of phase contrast mode in imaging lung tissue. We extend the FOV in a compact XTLI using a microarray anode-structured target source coupled with an inverse geometry, which eliminates grating G0 and relaxes the fabrication difficulty of G2. We believe the established design idea and imaging system would facilitate the wide applications of XTLI in high energy phase contrast imaging.

Fabrication of highly oriented Mn4N/Pt epitaxial bilayer structure on MgO(001) for spintronics applications

Magnetization control by electric current or spin current is an attractive technology with potential applications in memory and logic devices, and Mn4N has attracted much attention in recent years as a candidate material for current-induced magnetization switching and magnetic domain wall motion, as well as a skyrmion material. For these applications, a bilayer structure of Mn4N and heavy metals must be formed with high crystalline quality. In this study, we have achieved the epitaxial growth of Mn4N thin films on MgO(001) with a Pt underlayer. The reflection high-energy electron diffraction images and x-ray diffraction ω-rocking curves show that the Mn4N films fabricated on the Pt underlayers exhibit higher crystalline quality than the Mn4N films directly formed on the MgO. Furthermore, anomalous Hall effect loops with bump-like structures were observed. This is considered to be due to the topological spin texture. The Mn4N/Pt epitaxial bilayer structure has potential for use in spin–orbit torque applications and is a candidate configuration for topological spin textures.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden. The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2. The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation. The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

Monte Carlo model of a prototype flat-panel detector for multi-energy applications in radiotherapy.

The incorporation of multi-energy capabilities into radiotherapy flat-panel detectors offers advantages including enhanced soft tissue visualization by reduction of signal from overlapping anatomy such as bone in 2D image projections; creation of virtual monoenergetic images for 3D contrast enhancement, metal artefact reduction and direct acquisition of relative electron density. A novel dual-layer on-board imager offering dual energy processing capabilities is being designed. As opposed to other dual-energy implementation techniques which require separate acquisition with two different x-ray spectra, the dual-layer detector design enables simultaneous acquisition of high and low energy images with a single exposure. A computational framework is required to optimize the design parameters and evaluate detector performance for specific clinical applications. In this study, we report on the development of a Monte Carlo (MC) model of the imager including model validation. The stack-up of the dual-layer imager (DLI) was implemented in GEANT4 Application for Tomographic Emission (GATE). The DLI model has an active area of 43×43 cm2 , with top and bottom Cesium Iodide (CsI) scintillators of 600 and 800μm thickness, respectively. Measurement of spatial resolution and imaging of dedicated multi-material dual-energy (DE) phantoms were used to validate the model. The modulation transfer function (MTF) of the detector was calculated for a 120 kVp x-ray spectrum using a 0.5mm thick tantalum edge rotated by 2.5o . For imaging validation, the DE phantom was imaged using a 140 kVp x-ray spectrum. For both validation simulations, corresponding measurements were done using an initial prototype of the imager. Agreement between simulations and measurement was assessed using normalized root mean square error (NRMSE) and 1D profile difference for the MTF and phantom images respectively. Further comparison between measurement and simulation was made using virtual monoenergetic images (VMIs) generated from basis material images derived using precomputed look-up tables. The MTF of the bottom layer of the dual-layer model shows values decreasing more quickly with spatial frequency, compared to the top layer, due to the thicker bottom scintillator thickness and scatter from the top layer. A comparison with measurement shows NRMSE of 0.013 and 0.015 as well as identical MTF50 of 0.8 mm1 and 1.0 mm1 for the top and bottom layer respectively. For the DE imaging of the DE-phantom, although a maximum deviation of 3.3% is observed for the 10mm aluminum and Teflon inserts at the top layer, the agreement for all other inserts is less than 2.2% of the measured value at both layers. Material decomposition of simulated scatter-free DE images gives an average accuracy in PMMA and aluminum composition of 4.9% and 10.3% for 11-30mm PMMA and 1-10mm aluminum objects respectively. A comparison of decomposed values using scatter containing measured and simulated DE images shows good agreement within statistical uncertainty. Validation using both MTF and phantom imaging shows good agreement between simulation and measurements. With the present configuration of the digital prototype, the model can generate material decomposed images and virtual monoenergetic images.

Dual-Energy CT for Pediatric Thoracic Imaging: A Review.

Dual-energy CT has expanded the potential of thoracic imaging in both children and adults. Data processing allows material- and energy-specific reconstructions, which improve material differentiation and tissue characterization compared with single-energy CT. Material-specific reconstructions include iodine, virtual unenhanced, perfusion blood volume, and lung vessel images, which can improve assessment of vascular, mediastinal, and parenchymal abnormalities. The energy-specific reconstruction algorithm allows virtual monoenergetic reconstructions, including low-energy images to increase iodine conspicuity and high-energy images to reduce beam-hardening and metal artifacts. This review highlights dual-energy CT principles, hardware, and postprocessing algorithms; the clinical applications of dual-energy CT; and the potential benefits of photon counting (the most recently introduced iteration of spectral imaging) in pediatric thoracic imaging.

Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module.

Computed tomography (CT) is now universally applied into clinical practice with its non-invasive quality and reliability for lesion detection, which highly improves the diagnostic accuracy of patients with systemic diseases. Although low-dose CT reduces X-ray radiation dose and harm to the human body, it inevitably produces noise and artifacts that are detrimental to information acquisition and medical diagnosis for CT images. This paper proposes a Wasserstein generative adversarial network (WGAN) with a convolutional block attention module (CBAM) to realize a method of directly synthesizing high-energy CT (HECT) images through low-energy scanning, which greatly reduces X-ray radiation from high-energy scanning. Specifically, our proposed generator structure in WGAN consists of Visual Geometry Group Network (Vgg16), 9 residual blocks, upsampling and CBAM, a subsequent attention block. The convolutional block attention module is integrated into the generator for improving the denoising ability of the network as verified by our ablation comparison experiments. Experimental results of the generator attention module ablation comparison indicate an optimization boost to the overall generator model, obtaining the synthesized high-energy CT with the best metric and denoising effect. In different methods comparison experiments, it can be clearly observed that our proposed method is superior in the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and most of the statistics (average CT value and its standard deviation) compared to other methods. Because P<0.05, the samples are significantly different. The data distribution at the pixel level between the images synthesized by the method in this paper and the high-energy CT images is also most similar. Experimental results indicate that CBAM is able to suppress the noise and artifacts effectively and suggest that the image synthesized by the proposed method is closest to the high-energy CT image in terms of visual perception and objective evaluation metrics.

Nanocomposite scintillation perovskite-delignified wood photonic guides for X-ray imaging

Nanocrystalline scintillators with low-cost, fast response, and high light yield are widely used for X-ray imaging. However, they fail in high-energy X-ray imaging because of the fundamental limitations due to lateral radiation spreading in thicker device-compatible active layers. This issue is resolved by using the novel nanocomposite containing CsPbBr3 scintillation single-microcrystalites embedded into transparent, natural wood-derived light-guiding fibrous structures. The CsPbBr3 crystals convert X-ray to visible photons with a high photoluminescence quantum efficiency (76 %), and an average fast photoluminescence decay time of 25.9 ns. The delignified wood-derived fiber-like lumina effectively guide light, while suppressing the lateral spreading of scintillation photons. This unique porous nanocomposite material offers an excellent X-ray detection performance, and linear response in a wide parameter range. The developed scintillator screen achieves well-resolved, high-contrast X-ray imaging, featuring a very high spatial resolution of 11.9 lp/mm at a modulation transfer function of 0.2, thus exceeding the benchmark industry standards and offering a new advanced materials platform for radiation detection and dosimetry applications.

Radioluminescent Cu-Au Metal Nanoclusters: Synthesis and Self-Assembly for Efficient X-ray Scintillation and Imaging.

Zero-dimensional (0D) scintillation materials have drawn tremendous attention due to their inherent advantages in the fabrication of flexible high-energy radiation scintillation screens by solution processes. Although considerable progress has been made in the development of 0D scintillators, such as the current leading lead-halide perovskite nanocrystals and quantum dots, challenges still persist, including potential issues with self-absorption, air stability, and eco-friendliness. Here, we present a strategy to overcome those limitations by synthesis and self-assembly of a new class of scintillators based on metal nanoclusters. We demonstrate the gram-scale synthesis of an atomically precise nanocluster with a Cu-Au alloy core exhibiting high phosphorescence quantum yield, aggregation-induced emission enhancement (AIEE) behavior, and intense radioluminescence. By controlling solvent interactions, the AIEE-active nanoclusters were self-assembled into submicron spherical superparticles in solution, which we exploited as a novel building block for flexible particle-deposited scintillation films with high-resolution X-ray imaging performance. This work reveals metal nanoclusters and their self-assembled superstructures as a promising class of scintillators for practical applications in high-energy radiation detection and imaging.

Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography

Recently, some facilities have utilized the dual-energy subtraction (DES) technique for chest radiography to increase pulmonary lesion detectability. However, the availability of the technique is limited to certain facilities, in addition to other limitations, such as increased noise in high-energy images and motion artifacts with the one-shot and two-shot methods, respectively. The aim of this study was to develop artificial intelligence-based DES (AI–DES) technology for chest radiography to overcome these limitations. Using a trained pix2pix model on clinically acquired chest radiograph pairs, we successfully converted 130 kV images into virtual 60 kV images that closely resemble the real images. The averaged peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between virtual and real 60 kV images were 33.8 dB and 0.984, respectively. We also achieved the production of soft-tissue- and bone-enhanced images using a weighted image subtraction process with the virtual 60 kV images. The soft-tissue-enhanced images exhibited sufficient bone suppression, particularly within lung fields. Although the bone-enhanced images contained artifacts on and around the lower thoracic and lumbar spines, superior sharpness and noise characteristics were presented. The main contribution of our development is its ability to provide selectively enhanced images for specific tissues using only high-energy images obtained via routine chest radiography. This suggests the potential to improve the detectability of pulmonary lesions while addressing challenges associated with the existing DES technique. However, further improvements are necessary to improve the image quality.

Taking Bragg Coherent Diffraction Imaging to Higher Energies at Fourth Generation Synchrotrons: Nanoscale Characterization

High-energy Bragg coherent diffraction imaging (BCDI) can enable three-dimensional imaging of atomic structure within individual crystallites in complex environments. Here, we show that sufficient coherent photon flux is available to extend the BCDI technique to higher energies to (1) obtain improved strain information and sensitivity at the nanoscale with higher order Bragg reflections, (2) exploit BCDI in embedded materials or complex operando environments, (3) reduce X-ray induced sample modification, and (4) minimize dynamical scattering effects. We demonstrate the nanoscale imaging technique on the same sub-micrometer sized crystal at 8.5, 19.9, and 33.4 keV by taking advantage of the brilliance and coherence of the fourth generation Extremely Brilliant Source of the ID01 beamline at ESRF - The European Synchrotron (Grenoble, France). The photon flux, data quality, resolution and strain information are compared. We also show the first coherent diffraction measurements performed at the ID31-EBS beamline at an energy of 41 keV.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences.

Over several months, representatives from the U.S. Department of Energy (DOE) Office of Science and National Institutes of Health (NIH) had a number of meetings that lead to the conclusion that innovations in the Nation's health care could be realized by more directed interactions between NIH and DOE. It became clear that the expertise amassed and instrumentation advances developed at the DOE physical science laboratories to enable cutting-edge research in particle physics could also feed innovation in medical healthcare. To meet their scientific mission, the DOE laboratories created advances in such technologies as particle beam generation, radioisotope production, high-energy particle detection and imaging, superconducting particle accelerators, superconducting magnets, cryogenics, high-speed electronics, artificial intelligence, and big data. To move forward, NIH and DOE initiated the process of convening a joint workshop which occurred on July 12th and 13th, 2021. This Special Report presents a summary of the findings of the collaborative workshop and introduces the goals of the next one.

Laser active optical systems based on copper bromide active medium for high contrast and power images active formation

The paper presents the results of the development and research of the laser active optical system for formation the laser beam with the given intensity distribution (contrast) – high energy images. The system includes two active elements on coper bromide vapour with the excitations sources. For smoothly time shifting between laser pulses of each active element the synchronisation system is used. The dependence of the contrast and power of actively formed images on the time shift has been studied. It is shown that the maximum value of the image power and contrast is achieved with an identical shift between the generator and amplifier pulses.

High energy X-ray imaging of heterogeneity in charged and discharged lead-acid battery electrodes

Lead-acid battery technology continues to form a critical part of the global electrochemical energy storage market. Part of the reason for the lead-acid battery's success is due to its well understood electrochemistry. However, over recent years it has become clear that a poor understanding of inherent heterogeneity in chemical changes that happen during the charge and discharge cycles of these batteries is limiting innovation. In this paper we show that high-energy X-rays can be used to better understand how the chemistry within these systems is localised spatially, and how it affects battery performance. Used commercial lead-acid battery electrodes were sampled then imaged before and after the Pb K-edge (88 keV). In these electrodes, multiple features associated with battery failure were clearly observed throughout each electrode. A custom-made lead-acid battery was also imaged in-operando. Inhomogeneous utilization of the negative electrode arising from charge-transport effects could be clearly observed in the electrode face. These results show that high-energy X-ray imaging could be a powerful tool to better understand the relationship between the spatial and electrochemical dimensions in lead-acid batteries.

Shale Oil Occurrence Mechanisms: A Comprehensive Review of the Occurrence State, Occurrence Space, and Movability of Shale Oil

Shale oil resources are important supplements for the gradually decreasing oil production from conventional reservoirs. Although the exploitation and development of shale oil have achieved considerable progress in the last decade, the commercial extraction of hydrocarbons from shales is still difficult, especially in the lacustrine sedimentary basins of China. One of the key points controlling the successful extraction of hydrocarbons from shale systems is the understanding of the occurrence mechanism of shale oil. This study comprehensively summarizes the theories and techniques to characterize oil occurrence state, occurrence space, oil content, and oil movability in shale systems. Sophisticated instruments, such as high-resolution scanning electron microscopy and high-energy ray imaging, were utilized to qualitatively analyze the pore networks of shales. Advanced physical experiments and numerical simulation techniques, including step-by-step rock pyrolysis, solvent extraction, and NMR, were introduced to characterize shale oil adsorption and movability. By the comparative analysis of the occurrence space, it is found that the image observation technique especially focuses on concentrated pores, such as organic matter-hosted pores. The fluid injection technology yields particular pore size information, which should be calibrated using other information. The 3D digital core, demonstrating the spatial distribution of minerals and pores, is an effective input for shale oil flow simulation. Geological controls analysis about oil retention in organic-rich shales has found that the inorganic matter pores and fractures are probably the “sweet spot” of shale oil, due to the low oil adsorption and high light hydrocarbons content. Many physical experiments measure the total free oil content but neglect the hydrocarbon–rock interaction and the sequential migration of hydrocarbon compounds. Thus, micro-scaled experiments measuring the hydrocarbon adhesion forces are needed to uncover the occurrence mechanism of shale oil in the future.