CT Scan Data Research Articles

Left ventricular outflow tract eccentricity and the aortic annulus perimeter are strong predictors of permanent pacemaker implantation after self-expandable TAVR insertion

Abstract Introduction Transcatheter aortic valve replacement (TAVR) has become the treatment of choice for many patients with degenerative aortic stenosis. Nevertheless, need for permanent pacemaker implantation (PPI) continues being the Achilles heel after TAVR, especially for self-expanding valves. On the other hand, computed tomography (CT) scan provides several measurements with paramount importance for the success of the procedure. The aim of our study was to investigate whether CT-scan could help to identify patients at high risk for PPI need after TAVR. Methods We included all consecutive patients who underwent successful TAVR in a single center, since 2018 to 2022. Patients with previous PPI, "valve in valve" procedures, or without CT-scan measures of interest were excluded. Patients were followed-up at least 30 days after index procedure. Clinical, electrocardiographic and procedural characteristics were recorded, as well as CT-scan variables, such as annulus and left ventricle outflow tract (LVOT) perimeter and major and minor axis of both. Eccentricity index (EI) was calculated using the formula for the ellipse (the square root of 1-(minor axis/major axis)2), where the closer to 0 means more circular and the closer to 1 more eccentric ellipse. Multivariate binary logistic regression, including known clinical and electrocardiographic predictors of PPI after TAVR, along with CT-scan measurements, was done searching for the model best predicting PPI-need 30 days after TAVR. Results From the 297 patients undergoing TAVR in our centre during the study period, 106 were excluded because they met one of the excluding criteria (64 because of the lack of complete CT-scan data). Thus, 191 patients form the study population. 40 patients (20.9%) required a PPI. Characteristics of patients with/out PPI are shown at Table 1. After multivariate regression analysis, the best model predicting PPI included pre-existing complete right bundle branch block (RBBB) (HR: 3.86; 95%CI [1.35-11.02], p=0.013), larger valve size (HR (29-34 mm vs. 23-26 mm): 3.00; 95%CI [1.34-6.75], p=0.006) and LVOT EI (HR for every increase in 0.01: 0.96; 95%CI [0.94-0.99], p=0.021). C-statistic of the model was estimated in 0.762 (95%CI [0.677-0.847], p<0.001). ROC curve obtained with the model is shown at Figure 1. Conclusions Previous CT-scan measurements may help to identify patients at high risk for PPI after self-expanding TAVR. In our series, the strongest predictors for PPI were RBBB, LVOT EI (the lower the eccentricity, the higher the risk) and use of larger prostheses size (29 and 34 mm), which may be considered as a surrogate of the annular perimeter obtained by CT-scan.Baseline characteristicsROC curve

Inside out: Fusing 3D imaging modalities for the internal and external investigation of multi-material museum objects

3D imaging methods are increasingly employed in cultural heritage research to analyse and document objects in museum collections. In this work, we provide an interactive visualisation plugin for the open-source software Blender, to combine and inspect two complementary 3D imaging modalities: CT images, which capture the interior; and surface scans, which capture the exterior. 3D CT scan data can be visualised, both as volumetric representation and as orthogonal slices, and a 3D surface scan can be registered onto the CT data. It allows users to simultaneously and interactively inspect these modalities and to virtually cut through an object. It also provides tools for generating output images and videos for research and public outreach purposes. The plugin workflow was applied to four case studies from the collections of the Rijksmuseum, Amsterdam, and the British Museum, London. The plugin is published open-source together with detailed guidelines and a practice dataset.

QUALITY CONTROL CT SCAN (ANALISIS DAN EVALUASI KUALITAS CITRA)

Producing high-quality CT scans is crucial for accurately interpreting images and obtaining maximum diagnostic information. However, it usually comes at the cost of increased radiation exposure for the patient. Therefore, it's essential to evaluate CT scan image quality to ensure that it provides maximum information for diagnostic purposes while keeping the radiation dose received by the patient as low as possible. Several parameters can be used to evaluate the quality of CT scan images, such as accuracy, linearity, uniformity of the CT number, and high contrast resolution (spatial resolution). A phantom scan is carried out routinely once a week to test the quality of CT scan images. In this study, the image quality phantom was scanned using a Philips MX Evo 16 slice CT Scan over three months, which resulted in twelve CT scan image quality phantom image scan data. The CT scan data was processed and analyzed automatically using the IndoQCT application to determine the image quality. The value obtained was then compared with the test pass value stipulated in the Regulation of the Nuclear Energy Regulatory Agency (BAPETEN) Number 2 of 2018 concerning Compliance Tests for Diagnostic and Interventional Radiology X-ray. The average CT number accuracy value obtained was 0.6 HU, with a maximum value of 0.9 HU and a minimum value of 0.2 HU. The average CT number uniformity value obtained was 0.55 HU with a maximum value of 0.8 HU. The average CT number linearity value obtained was 0.9957 HU with a minimum of 0.9956. Meanwhile, the average MTF value obtained was 0.65, with a maximum value of 0.72. This study concludes that the values obtained still meet the CT Scan image quality test set by BAPETEN. Therefore, the CT Scan can still be considered reliable and used for service. Keywords: CT Scan, image quality, CT number, spatial resolution, IndoQCT.

Development of a Scoring Method Based on a Chest CT Scan to Determine the Outcomes of COVID-19 Patients.

Introduction As COVID-19 shifts from pandemic urgency to endemic management, healthcare systems are faced with the evolving challenge of providing optimized care and adept resource allocation in this evolving landscape of the disease. However, the timely management and accurate assessment of disease severity remains a cornerstone of effective treatment. This study presents a pioneering scoring system, based on the primary chest CT scan findings, to predict patient outcomes and to equip clinicians with a tool that can expedite decision-making. Method A retrospective cohort study was conducted involving 406 confirmed COVID-19 cases referred to two of our hospitals in Tehran, between February and April 2020. Radiographic and CT scan data were sourced from the imaging archive system and evaluated by a certified radiologist. We devised distinct severity scores for CT findings, demographic factors, and clinical indicators. These were synthesized into a comprehensive severity score to forecast critical patient outcomes, such as mortality, ICU admission, intubation, or extended hospitalization. Of the total cases, 161 (39.7%) were classified as severe, while 245 (60%) fell into the low or moderate severity category. Results The mean score of demographic, CT scan, and clinical characteristics was significantly higher for those in the severe COVID-19 than the non-severe group. The cutoff score for predicting the outcomes in COVID-19 patients for demographic, clinical, and chest CT scan factors was 2.5, 9.5, and 8.5, respectively. Multivariate analysis indicated that each unit increase in these scores elevated the odds of fatal outcomes by 24%, 2.8%, and 12%, respectively. Then, using the comprehensive severity score, which is the sum of the above scores, we further predicted the disease severity. Conclusion The findings suggest that our innovative scoring system, based on initial chest CT scan findings, serves as a robust predictor of COVID-19 outcomes.

Retromalleolar Groove Deepening in Recurrent Peroneal Tendon dislocation: Short- and Medium-Term Functional Outcomes

Recurrent peroneal tendon dislocation is a rare and often underrecognized condition. It may result from acute injuries, more commonly seen in athletes, or from untreated ankle sprains leading to chronic ankle instability. The aim of this study is to retrospectively evaluate short- and medium-term functional outcomes of the surgical technique involving deepening of the retromalleolar groove for the management of recurrent peroneal tendon dislocation. Nine patients, one of whom had bilateral dislocation, were enrolled in this study. The study group consisted of six males and three females, with a mean age of 31.2 years. CT scans were used to measure the depth of the neo-grooves, while the EFAS score (European Foot and Ankle Society Score) and AOFAS score (American Orthopedic Foot and Ankle Score) were used to assess functional results. The medium follow-up period was 4.8 years. CT scan data revealed a mean increase of 4.69 mm in the depth of the neo-retromalleolar groove. AOFAS scores improved from a mean preoperative value of 74.4 to 86.9 after surgery, and EFAS scores increased from a mean preoperative score of 19.7 to 31. Statistical analysis of clinical scores yielded significant results with a p-value < 0.005. All patients returned to their previous sports activities within an average time of 7.7 months, reporting no pain or limitations. There were no recurrences observed. The retromalleolar groove deepening technique demonstrated excellent results in the treatment of recurrent peroneal tendon dislocation, evidenced by significant improvements in clinical scores, functional recovery, successful return to sports, and high patient satisfaction.

Investigation of the Mechanical Response of the Foot Structure Considering Push-Off Angles in Speed Skating.

The push-off angle is an important factor affecting speed-skating performance. However, quantitative evidence for the relationship between the push-off angle and foot injury is incomplete. This study aimed to establish a three-dimensional (3D) finite element model (FEM) and investigate the mechanical responses of foot structures to stress and strain to explore the relationship between injury and movement. A 3D FEM was reconstructed using CT and 3D scan data and validated by comparing the FEM-predicted and in vivo measurement data in the balanced standing state. A push-off angle obtained from a video of a champion was loaded into the FEM. The error rates of validation were less than 10%. With a decrease in the push-off angle, the stress on the metatarsal increased; the stress on the talus, ankle joint cartilage and plantar fascia decreased, as did the strain on the ankle joint cartilage and plantar fascia. The FEM was considered reasonable. Not all foot structures had an increased risk of injury with a decrease in the push-off angle from 70° to 42°. The FEM established in this study provides a possibility for further determining and quantifying the relationship between foot injury and skating technique.

Prostate volume analysis in image registration for prostate cancer care: a verification study

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

Comparative analysis between different volumetric methods on measuring intracranial hemorrhage incorporating roundness index.

Intracranial hematoma (ICH) volume is considered a predictor of clinical outcome and mortality rate in ICH patients with traumatic brain injury (TBI). The ABC/2 method for ICH volume is the standard method used to date, however, its level of accuracy has been questioned in some studies. This study compared the performance of the ABC/2 method with planimetry and truncated pyramidal methods to highlight the potential of the planimetry method applied with automatic segmentation for evaluation of epidural hematoma (EDH) and intraparenchymal hematoma (IPH) volume. Six different phantoms were designed to evaluate the accuracy of volume estimation methods. 221 hematoma regions extracted from CT scans of 125 patients with head injury were also used to analyze the efficiency. The roundness index was utilized for the quantification of the ellipsoid-like shape. Regions of EDH and IPH on the CT scans were annotated by radiologists. The estimation errors for each method were statistically analyzed and compared. In addition, the relationship between the errors and roundness index was examined. The planimetry method showed the lowest relative error on phantom data. In the case of the CT scan data, the truncated pyramidal method resulted in the underestimation of the volumes of EDH and IPH. Meanwhile, the ABC/2, through principal component analysis (PCA) in the two-dimensional and PCA in the three-dimensional methods, resulted in a significant overestimation. In addition, both these approaches produced relative errors that showed a correlation with the roundness indexes for IPH. In comparison to other methods, the planimetry method had the lowest level of error with regards to calculation of the volume and it was also independent of the hematoma shape. The planimetry method, therefore, has the potential to serve as a useful tool for the assessment of ICH volume in TBI patients by using a deep learning system.

The Effect of the Hip Flexion Angle in Osteonecrosis of the Femoral Head Based on China-Japan Friendship Hospital Classification - A Finite Element Study.

The alteration in the mechanical environment of the necrotic area is the primary cause of the collapse observed in osteonecrosis of the femoral head (ONFH). This study aims to evaluate the biomechanical implications of the China-Japan Friendship Hospital (CJFH) classification system and hip flexion angles on the necrotic area in ONFH using finite element analysis (FEA). The goal is to provide valuable guidance for hip preservation treatments and serve as a reference for clinical diagnosis and therapeutic interventions. Hip tomography CT scan data from a healthy volunteer was used to create a 3D model of the left hip. The model was preprocessed and imported into Solidworks 2018, based on the CJFH classification. Material parameters and boundary conditions were applied to each fractal model in ANSYS 21.0. Von Mises stresses were calculated, and maximum deformation values were obtained to evaluate the biomechanical effects of the load on the necrotic area and post-necrotic femur, as well as assess each fractal model's collapse risk. (1) At the same hip flexion angle, maximum deformation followed this order: M Type < C Type < L Type. The L3 type necrotic area experienced the most significant deformation at 0, 60, and 110° angles (1.121, 1.7913, and 1.8239 mm respectively). (2) Under the same CJFH classification, maximum deformation values increased with hip flexion angle (0 < 60 < 110°), suggesting a higher risk of collapse at larger angles. (3) Von Mises stress results showed that the maximum stress was not located in the necrotic area but near the inner and outer edge of the femoral neck, indicating decreased stiffness and strength of the subchondral bone after osteonecrosis. The study found that femoral head collapse risk was higher when the necrotic area was located in the lateral column under the same stress load and flexion angle. Mechanical properties of the necrotic area changed, resulting in decreased bone strength and stiffness. Large-angle hip flexion is more likely to cause excessive deformation of the necrotic area; thus, ONFH patients should reduce or avoid large-angle hip flexion during weight-bearing training in rehabilitation activities.

A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.

The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.

Stress Propagation in the Craniofacial Skeleton on Frontal Impact-A Virtual Simulation Study.

The research paper emphasizes on virtual simulation of craniofacial skeleton to understand its Biomechanics. In this study, a 3D finite element model of the skull was created using CT scan data. All complexities of the skull geometry are simulated using ABAQUS software. The model was validated and then, subjected to frontal impact. Energy plots for the same were obtained. Impact analysis was done, and weak areas susceptible to fracture and hence, failure are identified. One of the emerging areas of applications of computational bio-mechanics is to understand the behavior of the skull during a traumatic injury, such as head impact during accidents. Finite element study is one such part where significant amount of research is being carried out to understand and predict the craniofacial injuries. For the head and brain trauma analysis, there are many software packages available, including ANSYS, LS-DYNA and ABAQUS. Research is being carried out towards developing high-fidelity human models, especially of the human skull to understand the bio-mechanical behavior when subjected to external impact. Impact analysis done in our study showed that a small change in impact velocity can result in a large change in damage that can happen. The study is expected to complement the existing treatment methodologies. Further, appropriate knowledge of fracture biomechanics can be used to design and develop safety measures in automobiles, sport guards, helmets to prevent and reduce facial injuries.

Influence of knee flexion on early femoral fracture healing: A combined analysis of musculoskeletal dynamics and finite elements

Background and ObjectivesKnee flexion causes a certain amount of misalignment and relative movement of the fractured ends of the femur fracture, and if the flexion angle is too large it will affect the stability of the fracture and the healing process, making it challenging to design a safe range of flexion. However, due to a lack of basic understanding of the effect of knee flexion on the mechanical environment at the fracture site, clinicians are often unable to provide an objective and safe range of motion in flexion based on subjective experience. The aim of this study was to evaluate the effect of knee flexion on plate and fracture healing using finite element analysis (FEA). MethodsA human musculoskeletal model was constructed based on CT scan data, and the mechanical properties of the fracture site were changed by adjusting the knee flexion angle. The joint forces, muscle forces and moments acting on the femur were obtained by inverse dynamics analysis, and the biomechanical properties of the fracture-plate system were analyzed using finite elements. A finite element model of the fracture-plate system without muscle loading was also constructed. The effect of knee flexion on the safety of plate fixation and fracture healing was evaluated in terms of the biomechanical properties of the plate and the interfragmentary motion of the fracture. ResultsAs the knee flexion angle increases, the von Mises stress of the locked compression plate (LCP) first increases, then decreases, then increases again. The deformation from compression bending to tension twisting occurs simultaneously. At 30° of flexion, shear interfragmentary motion (SIM) was dominant and inhibited fracture healing; at more than 45° of flexion, the plate was twisted and deformed to the lateral side of the body, and the fracture site underwent greater misalignment and relative motion, with destructive effects on bone scabs and healing tissues. If muscle loading is not taken into account, the plate will undergo predominantly bending deformation and will overestimate the interfragmentary strain in the far and near cortex. ConclusionsKnee flexion causes the plate to deform from compression bending to extension and torsion, which has an important impact on the safety and healing process of the fracture, and this study provides a biomechanical basis to guide the clinician in the post-operative rehabilitation of femoral fractures in the clinical setting.

Comparison of energy absorption in conventional and auxetic shoes: gait analysis and finite element modeling

A three-dimensional finite element (FE) model of the heel and shoe sole was developed to analyze the effects of materials with different Poisson's ratios for midsole on energy absorption and impact shock in the heel area during walking. The heel model was obtained from CT-scan data. The FE model was verified using Anybody software for barefoot with data obtained from gait analysis. Sole layers and plantar soft tissue were modeled, and five different Poisson's ratios (-0.4, -0.2, 0, 0.2, and 0.4) were compared for the midsole layer. In order to obtain the contact force, experiments were performed on a force plate using shoes with either auxetic or conventional midsole layer. Rayleigh damping theory was used to simulate viscous damping behavior in the midsole layer. The results showed that for materials with the same Young's modulus, there was less compression, energy absorption, and viscous dissipation energy in the auxetic model in comparison to the conventional model. However, when compressive strains of sole layers were the same (i.e., by changing Young's modulus), more energy absorption (about 9%) was observed for the midsole with negative Poisson's ratio with respect to the conventional midsole layer.

Toward Data-Model-Agnostic Autonomous Machine-Generated Data Labeling and Annotation Platform: COVID-19 Autoannotation Use Case

Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful &amp; quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (&#x0025;) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease.

Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan

BackgroundTo develop and validate classifier models that could be used to identify patients with a high percentage of potentially recruitable lung from readily available clinical data and from single CT scan quantitative analysis at intensive care unit admission. 221 retrospectively enrolled mechanically ventilated, sedated and paralyzed patients with acute respiratory distress syndrome (ARDS) underwent a PEEP trial at 5 and 15 cmH2O of PEEP and two lung CT scans performed at 5 and 45 cmH2O of airway pressure. Lung recruitability was defined at first as percent change in not aerated tissue between 5 and 45 cmH2O (radiologically defined; recruiters: Δ45-5non-aerated tissue > 15%) and secondly as change in PaO2 between 5 and 15 cmH2O (gas exchange-defined; recruiters: Δ15-5PaO2 > 24 mmHg). Four machine learning (ML) algorithms were evaluated as classifiers of radiologically defined and gas exchange-defined lung recruiters using different models including different variables, separately or combined, of lung mechanics, gas exchange and CT data.ResultsML algorithms based on CT scan data at 5 cmH2O classified radiologically defined lung recruiters with similar AUC as ML based on the combination of lung mechanics, gas exchange and CT data. ML algorithm based on CT scan data classified gas exchange-defined lung recruiters with the highest AUC.ConclusionsML based on a single CT data at 5 cmH2O represented an easy-to-apply tool to classify ARDS patients in recruiters and non-recruiters according to both radiologically defined and gas exchange-defined lung recruitment within the first 48 h from the start of mechanical ventilation.

3D recon, visualisation and 3D printing of the human ankle joint from CT scan data

3D recon, visualisation and 3D printing of the human ankle joint from CT scan data

Stress analysis of the screw of atlantoaxial posterior screw fixation C1LM-C2PS and C1LM-C2PARS with and without rod: A FEM based study

Human cervical spine consists of three vertebrae C0-occiput, C1-atlas, & C3-axial in its upper part. The atlantoaxial complex is the most portable segment of the cervical spine and is accountable for half of the flexion, extension, and axial rotation movements of the neck. Atlantoaxial cervical spine injuries are serious injuries since there is a likely risk of harm to the spinal cord. Sustaining stability of the atlantoaxial joint following a neck injury is a difficult task because both vertebrae have complicated anatomy. Thus, the strength of a bone screw is especially significant in the early post-operative stages. Different stabilization procedures have been accounted for C1-C2 injury fixation, out of which fixation with transarticular screws is considered as the gold standard procedure. But screw malposition and high riding transverse foramen of the C2 vertebra are some limitations of this fixation technique. Current FE-based study provides a comparison of the biomechanical stability of the two different atlantoaxial posterior screw fixation techniques (C1Lateral Mass-C2 Pedicle screws, C1Lateral Mass-C2PARS screws).In this work, a C1-C2 cervical spine model of a male patient is developed using MRI CT scan data using a 3-d slicer software and biomechanical stability comparison is done for C1LM-C2PS & C1LM-C2PARS screws fixed once with a supporting posterior screw fixation rod and once without rod under the physiological loading conditions of flexion, extension, lateral bending, and axial rotation using ANSYS WORKBENCH 16.2. The effects of the engaging supporting rod on the cervical spine were evaluated in the form of von-mises stresses induced in screws. Result shows reduction in screw stress values when implanted with supporting rods. C1LM-C2PS screw fixation with rod might be thought of as the best fixation solution for fractured C1-C2 vertebrae as it showed minimum stress values in the analysis as compared to C1LM-C2PARS with and without rod.

Keeping EV Lithium-Ion Batteries "Green" With CT-Scan Data Analysis

Abstract Fire risk in electric vehicle batteries can be reduced by detecting flaws through nondestructive visualization. Industrial computed tomography (CT scanning) combined with advanced software that makes sense of CT-generated images, allows users to measure voids and particle sizes within electrode active material during the research and development phase, detect delamination and contamination during cell manufacturing, analyze electrical connections and electrolyte fill levels, and provide many other quality-assurance functions that were once impractical or even impossible to perform.

Cranio-facial bones evaluation based on clinical CT data for sex determination in Northwest Indian population

The skull and pelvis have been the first choice of bones for determination of unknown human remains. The goal of the present study was to derive discriminant function equations by using clinical CT scan data of cranio-facial bones for sex determination in Northwest Indian population. This study was conducted at Department of Radiology, by collecting the retrospective data of CT scan of 217 samples. In the data, 106 were males and 111 were females in the age group between 20 and 80 years. The total number of parameters under investigation were 10. All the selected variables were sexually dimorphic and showed significant values. 91.7% of original grouped cases were correctly classified to their sex category. The TEM, rTEM and R were under the acceptable limits. The univariate, multivariate and stepwise discriminant function analysis recorded an accuracy of 88.9%, 91.7% and 93.6% respectively. Multivariate direct discriminant function analysis stepwise method yielded the highest level of accuracy in differentiating males and females. All the variables reflected statistically significant difference between males and females (p less than 0.001). The best single parameter with highest level of sexual dimorphic trait was length of cranial base. This study aims to provide sex assessment using clinical data of CT scan in Northwest Indian population by incorporating the BIOFB cranio-facial parameter. The morphometric measurements taken on CT scan images can be utilized by forensic experts in identification process.

Analysis Method of Full-Scale Pore Distribution Based on MICP, CT Scanning, NMR, and Cast Thin Section Imaging—A Case Study of Paleogene Sandstone in Xihu Sag, East China Sea Basin

Using different experimental methods, the pore radius ranges vary greatly, and most scholars use a single experiment to study pore structure, which is rarely consistent with reality. Moreover, the numerical models used in different experiments vary and cannot be directly compared. This article uniformly revised all experimental data into a cylinder model. Quantitative analysis of the full-scale pore distribution is established by mercury withdrawal–CT data, and semi-quantitative distribution is obtained by mercury–NMR–cast thin section imaging. In this paper, we introduce the tortuosity index (τ) to convert the CT ball-and-stick model into a cylinder model, and the pore shape factor (η) of the cast is used to convert the plane model into the cylinder model; the mercury withdrawal data is applied to void the influence of narrow throat cavities, and the NMR pore radius distribution is obtained using the mercury-T2 calibration method. Studies have shown that the thickness of bound water is 0.35~0.4 μm, so the pores with different radius ranges were controlled by different mechanisms in the NMR tests, with pores &lt; 0.35~0.4 μm completely controlled by surface relaxation, including strong bound water and weak bound water; pores in the 0.4~4 μm reange were controlled by surface relaxation; and pores &gt; 10 μm were completely controlled by free relaxation. The surface relaxivity rate of fine sandstone was 18~20 μm/s. The tortuosity index τ was generally 1~7; the larger the value, the more irregular the pores. The pore shape factor η was generally 0.2~0.5; the smaller the value, the more irregular the pores. Mercury withdrawal–CT scan data can quantitatively determine the pore radius distribution curve. The coefficient of the logarithm is positive considering porosity, and the constant is negative considering porosity. Permeability controls the maximum pore radius, with a max pore radius &gt; 100 μm and a permeability &gt; 1 mD. Mercury withdrawal–NMR–cast thin section imaging data can semi-quantitatively establish a pore radius distribution histogram. The histogram represents quasi-normal, stepped, and unimodal data. When 60 μm is the inflection point, if a large proportion of pores measure &gt; 60 μm, good reservoir quality is indicated. If a large proportion of pores measures &lt; 60 μm, the permeability is generally &lt;0.5 mD.