Computed Tomography Imaging Techniques Research Articles

Contemporary multimodality non-invasive cardiac imaging protocols for tetralogy of Fallot.

Tetralogy of Fallot is the most prevalent cyanotic congenital heart disease, requiring lifelong multimodality non-invasive cardiac imaging, such as echocardiography, cardiothoracic computed tomography, and cardiac magnetic resonance imaging. As imaging techniques continuously evolve and are gradually integrated into clinical practice, there is a critical need to update multimodality imaging protocols. Over the last two decades, cardiothoracic computed tomography imaging techniques have advanced remarkably, significantly enhancing its role in evaluating patients with tetralogy of Fallot. In this review, we describe contemporary multimodality non-invasive cardiac imaging protocols for tetralogy of Fallot, emphasizing the expanding role of cardiothoracic computed tomography. Additionally, we present standardized reporting forms designed to facilitate the clinical adoption of these protocols.

Anatomical studies on the PES region of Zebu cattle (Bos Taurus indicus) with special references to 3D computed tomography imaging technique

The 3D render volume reconstruction CT (3D-RVCT) produced detailed images of the PES region, determining its relationships with the surrounding structures. Despite extensive research in veterinary studies on the PES through gross anatomy and CT, there is a lack of studies on the PES of zebu cattle. The study aimed to analyze the PES of Zebu cattle using gross cross-sectional, radiographic, CT, and morphometric methods, with the use of 3D-RVCT to provide anatomical guidance for surgeons and students. The study was performed on sixteen PES regions to provide hard and soft tissues in CT images. Three are five tarsal bones and two large fused (III and IV) metatarsal bones that were completely fused except for their distal extremities, which were divided distally by the intertrochlear notch. The cortical thickness of the metatarsal bone was equal on both sides. The bony septum divided the medullary cavity between the two fused large metatarsal bones in the proximal distal half only and disappeared in the middle part. The reconstruction showed similar sizes in the right and left limbs, confirming the pes bones. The radiographic and CT images could be used as a normal reference for the interpretation of some clinical diseases in the PES. The 3D CT reconstruction of the pes bones was described by various CT oblique dorsal and plantar views. The study focuses on diagnosing PES disorders using CT imaging, improving medical interventions, improving Zebu cattle health outcomes, and empowering students to contribute to veterinary medicine research and advancements.

Paediatric Thoracic Imaging in Cystic Fibrosis in the Era of Cystic Fibrosis Transmembrane Conductance Regulator Modulation.

Cystic fibrosis (CF) is one of the most common progressive life-shortening genetic conditions worldwide. Ground-breaking translational research has generated therapies that target the primary cystic fibrosis transmembrane conductance regulator (CFTR) defect, known as CFTR modulators. A crucial aspect of paediatric CF disease is the development and progression of irreversible respiratory disease in the absence of clinical symptoms. Accurate thoracic diagnostics have an important role to play in this regard. Chest radiographs are non-specific and insensitive in the context of subtle changes in early CF disease, with computed tomography (CT) providing increased sensitivity. Recent advancements in imaging hardware and software have allowed thoracic CTs to be acquired in paediatric patients at radiation doses approaching that of a chest radiograph. CFTR modulators slow the progression of CF, reduce the frequency of exacerbations and extend life expectancy. In conjunction with advances in CT imaging techniques, low-dose thorax CT will establish a central position in the routine care of children with CF. International guidelines regarding the choice of modality and timing of thoracic imaging in children with CF are lagging behind these rapid technological advances. The continued progress of personalised medicine in the form of CFTR modulators will promote the emergence of personalised radiological diagnostics.

Usefulness of the spectral shaping dual-source computed tomography imaging technique in posterior corrective fusion for adolescent idiopathic scoliosis.

Since childhood exposure to radiation has been demonstrated to increase cancer risk with increase in radiation dose, reduced radiation exposure during computed tomography (CT) evaluation is desired for adolescent idiopathic scoliosis (AIS). Therefore, this retrospective study aimed to investigate the radiation dose of dual-source CT using a spectral shaping technique and the accuracy of the thoracic pedicle screw (TPS) placement for posterior spinal fusion (PSF) in patients with AIS. Fifty-nine female patients with thoracic AIS who underwent PSF using CT-guided TPSs were included and divided into two groups comprised of 23 patients who underwent dual-source CT (DSCT) with a tin filter (DSCT group) and 36 who underwent conventional multislice CT (MSCT group). We assessed the CT radiation dose using the CT dose index (CTDIvol), effective dose (ED), and accuracy of TPS insertion according to the established Neo's classification. The DSCT and MSCT groups differed significantly (p < 0.001) in the mean CTDIvol (0.76 vs. 3.31mGy, respectively) and ED (0.77 vs. 3.47mSv, respectively). Although the correction rate of the main thoracic curve in the DSCT group was lower (65.7% vs. 71.2%) (p = 0.0126), the TPS accuracy (Grades 0-1) was similar in both groups (381 screws [88.8%] vs. 600 screws [88.4%], respectively) (p = 0.8133). No patient required replacement of malpositioned screws. Spectral shaping DSCT with a tube-based tin filter allowed a 75% radiation dose reduction while achieving TPS insertion accuracy similar to procedures based on conventional CT without spectral shaping.

Case Report on Mucocele of Appendix with Tubo-ovarian Complex and Review of Literature

Mucocele of the appendix is a rare condition caused by mucus accumulation due to a blocked appendix resulting from inflammation, infection, or faecal matter blockage. It presents as lower abdominal pain, vomiting, nausea, abdominal mass, and distension. It is more common in individuals over 50 years old and is more prevalent in the elderly. Mucocele can be differentiated into mucinous cystadenocarcinoma, mucosal hyperplasia, and mucinous cystadenoma. It is often diagnosed incidentally during radiological, laparoscopic, or surgical interventions. Ultrasound and computed tomography imaging techniques are useful in screening and diagnosing appendiceal mucocele, as they can detect a cystic mass or soft tissue mass and differentiate between a mucocele and other neoplasms. Most cases are reported in older individuals, with few in young adults. In the present case report, the authors present a case of a 34-year-old female with a history of occasional abdominal pain. On investigation, it was found to be an inflamed appendix with a mass in the right iliac fossa for which the patient underwent appendectomy. Due to the involvement of the right adnexa with tubo-ovarian complex and omentum, the present case presents a unique manifestation of an appendix mucocele.

Tomographic reconstruction strategies for nondestructive testing with a commercial CT scanner

In this work, a framework was developed to access and process raw data from a commercial X-ray Computed Tomography (CT) scanner for research purposes. Our method requires vendor-provided binaries to convert the data to a readable format and also to remove the effect of proprietary beam hardening preprocessing. As a result, custom reconstruction techniques, including beam-hardening corrections algorithms, can be applied. Small region-of-interest CT imaging techniques and different backprojection algorithms were investigated to improve image quality (spatial resolution, noise) with an in-house iterative reconstruction algorithm. For a reconstruction matrix of 512 pixels × 512 pixels, processing times of approximately 2.5 s per slice were obtained using a set of 8 x GPUs. With this framework, high-quality images of high density samples (e.g., minerals) can be obtained with reduced truncation-induced blurring, free of artifacts stemming from the reconstruction process and reduced beam-hardening artifacts.

Review of shock wave pressure reconstruction methods in explosion field

Explosion shock wave pressure is one of the main damage parameters produced in the process of ammunition explosion, and it is also an important technical index to evaluate the damage power of ammunition. However, in the actual testing process, only a limited amount of shockwave pressure data at specific measuring points can be obtained, which cannot accurately reflect the distribution laws of the shockwave pressure propagation after the ammunition explosion. Therefore, it is particularly important to conduct reconstruction of the shockwave pressure field distribution laws based on limited measuring point data. This paper reviews the research work and related achievements obtained by researchers at home and abroad on the reconstruction of shockwave pressure distribution laws in explosion fields. The paper also elucidates the relevant application of computer tomography imaging techniques, shockwave propagation attenuation model reconstruction, and various interpolation algorithms in the reconstruction process of the shockwave pressure field. Given the current research situation both domestically and internationally, we have pinpointed the major issues that still exist in the current stage of research and proposed the key areas of focus that need attention in future research.

Operando ultra-high-resolution X-ray microscopy of lithium anodes with separator interactions

Lithium (Li) metal is an attractive anode material candidate for high-energy-density battery systems that could enable challenging electrification applications, such as flight and heavy-duty vehicles. However, the formation of porous deposits and dendrites during cycling cause capacity loss and internal short-circuit risks. Here, we use high-resolution in-situ/operando Zernike phase contrast X-ray microscopy to directly visualize the lithium electrodeposition process inside a coin cell with a commercial separator. The nucleation and subsequent lithium electrodeposition were imaged by transmission X-ray microscopy (TXM) at 150 nm spatial resolution and <2 min temporal resolution. The resulting lithium deposits were then reconstructed into three-dimensional (3D) volumes by computed tomography (CT), revealing the Li structures above, at, and below the anode/separator interface. Distinct nucleation and deposition mechanisms were observed under varied cell temperatures, charging current density, and electrolyte composition. This work demonstrates the unique strengths of combining TXM and CT imaging techniques: the dynamic evolution of lithium electrodeposition and the comprehensive morphology of the resulting structure were characterized together. Furthermore, our results indicate the importance of in-situ/operando characterization of systems in commercially-relevant configurations in developing practical dendrite mitigation strategies.

Quantification of six-degree-of-freedom motion during beam delivery in spine stereotactic body radiotherapy using intra-irradiation cone-beam computed tomography imaging technique

PurposeQuantifying intra-fractional six-degree-of-freedom (6DoF) residual errors or motion from approved patient setups is necessary for accurate beam delivery in spine stereotactic body radiotherapy. However, previously reported errors were not acquired during beam delivery. Therefore, we aimed to quantify the 6DoF residual errors and motions during arc beam delivery using a concurrent cone-beam computed tomography (CBCT) imaging technique, intra-irradiation CBCT. MethodsConsecutive 15 patients, 19 plans for various treatment sites, and 199 CBCT images were analyzed. Pre-irradiation CBCT was performed to verify shifts from the initial patient setup using the ExacTrac system. During beam delivery by two or three co-planar full-arc rotations, CBCT imaging was performed concurrently. Subsequently, an intra-irradiation CBCT image was reconstructed. Pre- and intra-irradiation CBCT images were rigidly registered to a planning CT image based on the bone to quantify 6DoF residual errors. Results6DoF residual errors quantified using pre- and intra-irradiation CBCTs were within 2.0 mm/2.0°, except for one measurement. The mean elapsed time (mean ± standard deviation [min:sec]) after pre-irradiation CBCT to the end of the last arc beam delivery was 6:08 ± 1:25 and 7:54 ± 2:14 for the 2- and 3-arc plans, respectively. Root mean squares of residual errors for several directions showed significant differences; however, they were within 1.0 mm/1.0°. Time-dependent analysis revealed that the residual errors tended to increase with elapsed time. ConclusionThe errors represent the optimal intra-fractional error compared with those acquired using the pre-, inter-beam, and post-6DoF image guidance and can be acquired within a standard treatment timeslot.

An Innovative Low-dose CT Inpainting Algorithm based on Limited-angle Imaging Inpainting Model.

With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public's attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers. To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model-Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images. In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection. We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score. Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.

Titanium porous-transport layers for PEM water electrolysis prepared by tape casting

While the porous-transport layer (PTL) is a key component in PEM electrolyzers, it is one of the most underexplored due to limited available structures. In this work, we present a novel PTL design for PEM water electrolyzers enabled by a cost-effective, scalable tape-casting technique. A precise control of the PTL pore structure is achieved by incorporating poreformers of various sizes, and by varying the titanium and poreformer ratio. The structures are characterized with SEM and synchrotron X-ray computed tomography imaging techniques. Comprehensive electrochemical performance analysis demonstrates that higher titanium loading provides improved contact at the catalyst-layer/PTL interface but suffers from severe mass-transport losses due to gas bubbles. We solve this mass-transport problem by mixing in large poreformer beads that produce a highly porous structure with excellent gas removal properties yet still maintaining mechanical integrity. The PTL fabricated with 60:40 Ti:PMMA ratio and 60 μm PMMA bead size outperformed the standard commercial Ti powder-based PTL by 62 mV at 4 A/cm2.

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.

In vivo imaging of brown adipose tissue vasculature reactivity during adrenergic stimulation of non-shivering thermogenesis in mice.

Brown adipose tissue (BAT) is a fat tissue specialized in heat production (non-shivering thermogenesis) and used by mammals to defend core body temperature when exposed to cold. Several studies have shown that during non-shivering thermogenesis the increase in BAT oxygen demand is met by a local and specific increase in tissue's blood flow. While the vasculature of BAT has been extensively studied postmortem in rodents using histology, optical and CT imaging techniques, vasculature changes during stimulation of non-shivering thermogenesis have never been directly detected in vivo. Here, by using computed tomography (CT) angiography with gold nanoparticles we investigate, non-invasively, changes in BAT vasculature during adrenergic stimulation of non-shivering thermogenesis by norepinephrine, a vasoconstrictor known to mediate brown fat heat production, and by CL 316,243, a specific β3-adrenergic agonist also known to elicit BAT thermogenesis in rodents. We found that while CL 316,243 causes local vasodilation in BAT, with little impact on the rest of the vasculature throughout the body, norepinephrine leads to local vasodilation in addition to peripheral vasoconstriction. As a result, a significantly greater relative increase in BAT perfusion is observed following the injection of NE compared to CL. This study demonstrates the use of in vivo CT angiography as an effective tool in assessing vascular reactivity in BAT both qualitatively and quantitatively in preclinical studies.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19.The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Unsupervised Deep learning-based Feature Fusion Approach for Detection and Analysis of COVID-19 using X-ray and CT Images

Aims: This study investigates an unsupervised deep learning-based feature fusion approach for the detection and analysis of COVID-19 using chest X-ray (CXR) and Computed tomography (CT) images. Background: The outbreak of COVID-19 has affected millions of people all around the world and the disease is diagnosed by the reverse transcription-polymerase chain reaction (RT-PCR) test which suffers from a lower viral load, and sampling error, etc. Computed tomography (CT) and chest X-ray (CXR) scans can be examined as most infected people suffer from lungs infection. Both CT and CXR imaging techniques are useful for the COVID-19 diagnosis at an early stage and it is an alternative to the RT-PCR test. Objective: The manual diagnosis of CT scans and CXR images are labour-intensive and consumes a lot of time. To handle this situation, many AI-based solutions are researched including deep learning-based detection models, which can be used to help the radiologist to make a better diagnosis. However, the availability of annotated data for COVID-19 detection is limited due to the need for domain expertise and expensive annotation cost. Also, most existing state-of-the-art deep learning-based detection models follow a supervised learning approach. Therefore, in this work, we have explored various unsupervised learning models for COVID-19 detection which does not need a labelled dataset. Methods: In this work, we propose an unsupervised deep learning-based COVID-19 detection approach that incorporates the feature fusion method for performance enhancement. Four different sets of experiments are run on both CT and CXR scan datasets where convolutional autoencoders, pre-trained CNNs, hybrid, and PCA-based models are used for feature extraction and K-means and GMM techniques are used for clustering. Results: The maximum accuracy of 84% is achieved by the model Autoencoder3-ResNet50 (GMM) on the CT dataset and for the CXR dataset, both Autoencoder1-VGG16 (KMeans and GMM) models achieved 70% accuracy. Conclusion: Our proposed deep unsupervised learning, feature fusion-based COVID-19 detection approach achieved promising results on both datasets. It also outperforms four well-known existing unsupervised approaches.

Prevalence and Features of Maxillary Sinus Septa in Patients with Cleft Lip and Palate: Cone Beam Computed Tomography Imaging Technique.

The development of the maxillary sinus is different in patients with cleft lip and palate (CLP) compared to non-CLP individuals. To investigate the prevalence and features of maxillary sinus septa (MSS) in patients with CLP in comparison with the non-CLP population. Retrospective study. Cone beam computed tomography (CBCT) evaluation. CLP center in Shiraz faculty of dentistry, Iran. A total 306 sinuses (88 cleft and 218 noncleft) on 153 images (CLP group: n = 66; control group: n = 87) were examined to determine the prevalence of septa and characterize them. Sinus septa were characterized according to height, orientation, angle, origin, and location. The chi-square test, Mann-Whitney U test, and Fisher's exact test were used for statistical analysis. The prevalence of septa was 28.9% and 32.1% in the CLP and control groups, respectively. No significant difference was found between the study groups in terms of prevalence, location, and orientation of MSS. The average height and angle of septa were significantly higher in the control group compared to the CLP group. Inferior origin was significantly more prevalent in the control group than in the CLP group (P = .004). There was no difference in the prevalence of MSS between patients with CLP and non-CLP individuals. However, certain features of the septa were different in patients with CLP.

Cerebral Vascular Density and Its Possible Correlation with Alzheimer Disease Progression in Elderly Individuals with Rheumatoid Arthritis

BackgroundRheumatoid Arthritis (RA) is characterized by chronic systemic inflammation, primarily affecting the joints. In RA patients, high concentrations of inflammatory cytokines are found within the synovial fluid and serum (PMID: 1700672). These cytokines reduce the blood brain barrier (BBB) integrity by decreasing the expression of tight junction proteins (PMID: 29910805). In a RA animal model such decreased BBB integrity has been shown to affect the transport of amyloid beta peptide (Aβ) between the peripheral circulation and the brain, thereby promoting accumulation of Aβ within the cerebral vasculature followed by the endothelial cell degeneration (PMID: 22029666). In humans, a similar pathologic process is a major risk factor for Alzheimer’s Disease (AD) which accelerates neuronal loss and cognitive impairment associated with AD (PMID: 29377008). Assessment of cerebral vascular density is an emerging modality for detection and monitoring the progression of cerebrovascular disease (CVD) (PMID: 15564412). Advancements in computed tomography (CT) imaging technology and the development of novel contrast agents should provide more accurate assessment for CVD in humans.The main objective of this study was to test high‐resolution CT imaging using a recently developed BriteVu contrast agent in conjunction with conventional histological techniques for postmortem assessment of CVD and pathology in the cerebral cortex and hippocampus of two male cadavers. The first, 74‐year‐old (D1) was diagnosed with RA and AD, while the second, 90‐year‐old (D2) had RA.ResultsThe D1 and D2 cadavers were fixed and perfused with BriteVu contrast agent. Heads were removed and scanned with a VimagoGT30 CT scanner at 300 µm. The images were subjected to volumetric analysis using Radiant software. The results showed that scans of the cerebral cortex from D2, specifically the middle frontal, inferior parietal, and superior temporal gyri, had significantly lower mean Hounsfield unit densities (MHUD) than those from the same regions of D1. The MHUD from the hippocampal scans revealed no significant difference. The lower MHUD count in the D2 cortex in comparison to D1 most likely reflects a lower vascular density in the respective brain region. The AD related pathology in both donors was assessed by histochemical and immunohistochemical techniques. To that end, the brains were extracted and fixed in formalin solution for 12‐16 weeks. The brain hippocampal and cortical sections were paraffin processed followed by H&amp;E and immunohistochemical staining for Aβ 1‐42, phospho‐tau, and phospho‐TDP‐43. Analysis of the data confirmed the AD diagnosis in D1 and demonstrated distinct AD pathology in D2. The AD staging could be characterized as mild in D1 and intermediate in D2.Conclusionsi) The vascular density within the cerebral cortex of elderly individuals with RA could correlate with the AD progression. ii) The adaptation of the reported CT imaging technique for its use in live individuals could provide more sensitive and accurate method for assessing AD in RA patients at the early AD stage(s).

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence.

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

Mitigation of Mineral Deposition in Carbonate and Sandstone Rocks Using Green Scale Inhibitors.

Waterflooding is potentially a viable approach to enhance oil recovery, though its efficacy can profoundly be compromised due to formation damage as a result of inorganic scale deposition. In this study, a series of high-temperature core flooding experiments were conducted to evaluate two green scale inhibitors (SIs) of folic acid and inulin as alternative inhibitors to mitigate mineral deposition. The co-injection of two incompatible brines (with and without SIs) for two flow rates of 0.5 and 3 mL/min into two core samples of dolomite and sandstone was experimentally investigated. The results showed that folic acid would inhibit scale formation as much as 45-49%, at the lower flow rate, compared to inulin with an efficiency of 29-39%, at the higher flow rate. Moreover, computed tomography imaging technique showed that scale formation and fine migration would be dominant mechanisms for formation damage in dolomite and sandstone rocks, respectively. The theoretical study based on surface energy also confirmed the experimental results in terms of the work of adhesion which showed that folic acid would mitigate the calcite deposition on rock surfaces approximately 55%. Finally, a phosphonate-based commercial SI was compared with the green SIs which reaffirmed their potencies.

Use of computer tomography imaging for analyzing bone remodeling around a percutaneous osseointegrated implant.

Osseointegration (OI) is being used for the direct skeletal attachment of prosthetic limbs using an intramedullary stem that extends percutaneously from the subject's residual limb. For this technology to be successful, bone ingrowth and remodeling around the implant must occur. Physicians need an effective way to assess bone remodeling to make informed treatment and rehabilitation decisions. Previous studies utilizing two-dimensional imaging X-ray as a tool to monitor bone-remodeling around OI devices have limitations. This study describes methodology that was developed utilizing computed tomography (CT) imaging as a tool for analyzing bone remodeling around a percutaneous OI implant. Six transfemoral amputees implanted with a percutaneous osseointegrated prosthesis (POP) had CT scans taken of their residual femur at 6 and 52 weeks postoperatively. Three-dimensional femoral models were processed using custom MATLAB script to collect cortical and medullary morphology measurements. Morphology data from 6-and 52-week scans were compared to quantify bone remodeling around the POP implant. Fifty-two weeks after implantation of the POP device, increases in cortical bone area and thickness were observed around the porous-coated stem. Minimal changes were observed in the medullary canal parameters within the periprosthetic regions. This study successfully utilized CT imaging and three-dimensionalmodeling techniques to analyze longitudinal data of bone remodeling around a transfemoral percutaneous implant. These methods have the potential to be used as a clinical tool for evaluating orthopedic implants in vivo. Data collected suggests that the POP device achieved the desired bone remodeling around the porous-coated region of the implanted stem.