CT Scan Images Research Articles

Coronary plaque volume burden and composition in patients with non-hemodynamically significant coronary artery plaques and systemic inflammation

Abstract Introduction Atherosclerotic coronary disease is a leading cause of morbidity and mortality worldwide. Systemic inflammation can affect plaque composition and thus increase cardiovascular risk. Identifying plaque characteristics in these patients could be useful to learn how inflammation (identified by easy to access parameters, like high ferritin and CRP) affects plaque composition in these challenging patients. Purpose We aimed to evaluate the association of systemic inflammation identified by high ferritin and/or high C-reactive protein (CRP) with coronary plaque volume and composition in patients with non-hemodynamically significant coronary artery plaques (stenosis <50%). Methods We prospectively recruited 109 patients referred for noninvasive CT angiography (from November 2023 to January 2024) with suspected coronary artery disease. 20 had totally/partially non-calcified, non-significant coronary plaques (stenosis <50%). Plaque volume was measured. Plaque components were stablished as calcified (>200 UH), lipidic (<60 UH) and fibrous (60-200 UH). Systemic inflammation was identified when ferritin was >300 ng/mL and/or C-reactive protein CRP was >5 mg/L, according to our laboratory high normal limits. CT scan images were acquired with a 128 detectors dual-layer CT scan with prospective-retrospective/synchronization. Intravascular iodine-based contrast agent was administered. Results Mean age was 60 ± 11 years old and 75% of patients were men. 55% had high blood pressure, 65% were current or previous smokers and 40% had dyslipidemia (35% received statins). 10% were diabetic. There were no difference between groups for these characteristics. We analyzed 33 plaques. Fibrous plaque volume was found to be significantly higher in patients with high ferritin (66.9 vs 32.5 mm3; p = 0.04) and in patients with high CRP (78.7 vs 36.4 mm3; p = 0.05). The percentage of fibrous plaque tended to be higher in patients with higher ferritin and CRP (54.9 vs 38.9; p = 0.06 and 60.6 vs 40.7%; p = 0.07, respectively). Conclusions Systemic inflammation affecting coronary plaque composition in non-hemodynamically significant coronary atherosclerotic disease can by identified by high ferritin and high CRP levels. It is associated with higher fibrous plaque volumes. This finding suggest that systemic inflammation can associate more vulnerable plaques and might benefit from more aggressive management of non-hemodynamically significant atherosclerosis.TableFigure

The Effect of the COVID-19 Pandemic on the Working Conditions and Efficiency of Radiographers at Ahvaz Jundishapur University of Medical Sciences' Radiology and CT Scan Imaging Centers

Background: Radiographers are highly skilled imaging technology users who work directly with COVID-19 cases. Objectives: This study aims to assess the impact of COVID-19 on the working conditions and efficiency of radiographers and their level of satisfaction with personal protective equipment (PPE), cleaning supplies, and facilities during the COVID-19 epidemic. Methods: A prospective observational study was conducted among radiographers registered in the radiology and CT scan imaging centers of Ahvaz Jundishapur University of Medical Sciences and some private centers between June and October 2022. A questionnaire with 28 multiple-choice questions was used to collect information. The questionnaire consisted of two sections; the first part contained demographic information. In the second part, participants answered questions about changes in working conditions, such as changes in stress level, work efficiency, the need for more rest, imaging time, use of equipment, and personal protective equipment at work, based on their experiences and observations during the COVID-19 pandemic. The data analysis was performed using Statistical Product Service Solutions (SPSS) software, version 26.0, with statistical significance assumed at a P-value < 0.05. Results: One hundred and fifty radiographers completed the questionnaire. The mean age and mean work experience of participants were 33.2 ± 8.3 years and 9 ± 7.2 years, respectively. The results showed that most participants of both sexes agreed that they needed more rest (P-value < 0.05); their work efficiency had not decreased significantly during the COVID-19 epidemic. The stress level of participants had significantly increased compared to before the epidemic. There were significant differences between government-owned hospitals and private radiology centers regarding the presence of a COVID-19-related protocol, the compulsory use of masks, the existence of decontamination instructions, the type of air decontamination, the contact with imaging equipment manufacturers, the timing of decontamination, and the type of decontamination applied to the imaging equipment (P-value < 0.05). Conclusions: This study demonstrated that the reduction in work efficiency among radiographers was not significant compared to before the COVID-19 pandemic. According to the study's results, radiographers' working conditions underwent numerous adjustments over the course of the COVID-19 epidemic.

A clinical comparison of a digital versus conventional design methodology for transtibial prosthetic interfaces

A transtibial prosthetic interface typically comprises a compliant liner and an outer rigid socket. The preponderance of today’s conventional liners are mass produced in standard sizes, and conventional socket design is labor-intensive and artisanal, lacking clear scientific rationale. This work tests the clinical efficacy of a novel, physics-based digital design framework to create custom prosthetic liner-socket interfaces. In this investigation, we hypothesize that the novel digital approach will improve comfort outcomes compared to a conventional method of liner-socket design. The digital design framework generates custom transtibial prosthetic interfaces starting from MRI or CT image scans of the residual limb. The interface design employs FEA to simulate limb deformation under load. Interfaces are fabricated for 9 limbs from 8 amputees (1 bilateral). Testing compares novel and conventional interfaces across four assessments: 5-min walking trial, thermal imaging, 90-s standing pressure trial, and an evaluation questionnaire. Outcome measures include antalgic gait criterion, skin surface pressures, skin temperature changes, and direct questionnaire feedback. Antalgic gait is compared via a repeated measures linear mixed model while the other assessments are compared via a non-parametric Wilcoxon sign-rank test. A statistically significant (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P=0.032$$\\end{document}) decrease in pain is demonstrated when walking on the novel interfaces compared to the conventional. Standing pressure data show a significant decrease in pressure on novel interfaces at the anterior distal tibia (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P = 0.012$$\\end{document}), with no significant difference at other measured locations. Thermal results show no statistically significant difference related to skin temperature. Questionnaire feedback shows improved comfort on novel interfaces on posterior and medial sides while standing and the medial side while walking. Study results support the hypothesis that the novel digital approach improves comfort outcomes compared to the evaluated conventional method. The digital interface design methodology also has the potential to provide benefits in design time, repeatability, and cost.

The Effect of Tube Voltage Variations on CT Scan Image Quality at Prof. I G. N. G. Ngoerah General Hospital, Denpasar, Indonesia

Aims: Knowing the SNR and CNR values obtained from the image results that have been varied in the tube voltage. Knowing the effect of variations in the tube voltage value on the quality of the CT scan image that will be produced. Place and Duration of Study: Radiology installation of Prof. I G. N. G. Ngoerah General Hospital, Denpasar from May 2024 to August 2024. Methodology: This study used a Canon Aquilion LB CT Scanner to scan a TOS phantom containing 6 materials (polypropylene, nylon, acrylic, derlin, air, and water) with varying tube voltages (80 kV, 100 kV, 120 kV, 135 kV). The data were analyzed using PSPP and Excel to calculate SNR and CNR, and statistical tests for normality, Pearson correlation, and simple regression were performed. Results: The image data analyzed includes the average value of the object ROI, the average ROI background, and the standard deviation of the background. The data is grouped based on the variation of the tube voltage, the average value and standard deviation of the ROI on each material, and the standard deviation of the background. The results obtained for the relationship between the variation of the tube voltage and the SNR value are that the tube voltage affects the SNR value by 94,02% in polypropylene, 97,79% in nylon, 97,71% in acrylic, 96,42% in derlin, and 94,22% in air. And for the relationship between the tube voltage and the CNR value, the tube voltage affects the CNR value by 93,65% in polypropylene, 97,35% in nylon, 96,81% in acrylic, 95,62% in derlin, and 94,65% in air. Conclusion: Selecting the right tube voltage is important to optimize CT scan image quality, with 120 kV and 135 kV recommended for organs such as the kidney, liver, and lungs as they produce better SNR and CNR. Organs with significant attenuation differences show increased SNR and CNR, while soft tissues such as fat experience decreased contrast at high voltages. The use of tube voltage must be adjusted to diagnostic needs and evaluated through quality control according to BAPETEN regulation Number 2 of 2018.

Prediction of Transport and Deposition of Porous Particles in the Respiratory System Using Eulerian-Lagrangian Approach.

Deep lung delivery is crucial for respiratory disease treatment. Although nano and submicron particles exhibited a good deposition efficiency in deep regions of the lung, powder nonuniformity and particle agglomeration reduce their efficiency. Inhalation of porous particles (PPs) can overcome the mentioned challenges due to their larger size and low-density. The present study numerically investigates the deposition and penetration efficiency of orally inhaled PPs. A revised drag coefficient was implemented for PP transport. A realistic mouth-throat to the fifth generation of the lung was reconstructed from CT-scan images. A dilute suspension of uniformly distributed particles was considered at three inhalation flow rates (15, 30, and 45 L/min). Governing equations of the flow field and particle transport are solved using an Eulerian-Lagrangian approach. The results demonstrate that inhaling PPs significantly reduces the total and regional deposition of particles. There was also a critical porosity value under moderate and high inhalation flow rates for large PPs. Below this critical value, PP deposition efficiency substantially decreases. Additionally, it was also found that under low inhalation flow rates, the impact of porosity value is negligible. Almost 95% of the PPs penetrate the lower branches. These findings provide particle engineers and pharmaceutics with profound insight into developing novel inhalation techniques and drug delivery methods for deep lung delivery.

Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images

In light of the ongoing battle against COVID-19, while the pandemic may eventually subside, sporadic cases may still emerge, underscoring the need for accurate detection from radiological images. However, the limited explainability of current deep learning models restricts clinician acceptance. To address this issue, our research integrates multiple CNN models with explainable AI techniques, ensuring model interpretability before ensemble construction. Our approach enhances both accuracy and interpretability by evaluating advanced CNN models on the largest publicly available X-ray dataset, COVIDx CXR-3, which includes 29,986 images, and the CT scan dataset for SARS-CoV-2 from Kaggle, which includes a total of 2,482 images. We also employed additional public datasets for cross-dataset evaluation, ensuring a thorough assessment of model performance across various imaging conditions. By leveraging methods including LIME, SHAP, Grad-CAM, and Grad-CAM++, we provide transparent insights into model decisions. Our ensemble model, which includes DenseNet169, ResNet50, and VGG16, demonstrates strong performance. For the X-ray image dataset, sensitivity, specificity, accuracy, F1-score, and AUC are recorded at 99.00%, 99.00%, 99.00%, 0.99, and 0.99, respectively. For the CT image dataset, these metrics are 96.18%, 96.18%, 96.18%, 0.9618, and 0.96, respectively. Our methodology bridges the gap between precision and interpretability in clinical settings by combining model diversity with explainability, promising enhanced disease diagnosis and greater clinician acceptance.

Measurements of Stray Radiation from Medical Devices in Hospitals

The main objective of this study was to look for the electromagnetic interference produced by medical devices in hospitals and to evaluate its impacts on the proper functions of such devices. However, the present study encountered other important problems with regard to CT scanner and X-ray Imaging systems. Such problems demand fast responses in order to secure health safety for the patients and the operators of such systems. ORIGINAL RESEARCH ARTICLE | Oct. 23, 2024

Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients’ 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients’ lives.

Improving lung cancer diagnoses: a machine learning approach for detection and prediction in CT-Scan image analysis

Improving lung cancer diagnoses: a machine learning approach for detection and prediction in CT-Scan image analysis

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Background The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early detection and quick isolation of infections. Several diagnostic tools are currently being used for COVID-19 detection using computed tomography (CT) scan and chest X-ray (CXR) images. Methods In this study, a novel deep learning-based model is proposed for rapid detection of COVID-19 using CT-scan images. The model, called pre-trained quantum convolutional neural network (QCNN), seamlessly combines the strength of quantum computing with the feature extraction capabilities of a pre-trained convolutional neural network (CNN), particularly VGG16. By combining the robust feature learning of classical models with the complex data handling of quantum computing, the combination of QCNN and the pre-trained VGG16 model improves the accuracy of feature extraction and classification, which is the significance of the proposed model compared to classical and quantum-based models in previous works. Results The QCNN model was tested on a SARS-CoV-2 CT dataset, initially without any pre-trained models and then with a variety of pre-trained models, such as ResNet50, ResNet18, VGG16, VGG19, and EfficientNetV2L. The results showed the VGG16 model performs the best. The proposed model achieved 96.78% accuracy, 0.9837 precision, 0.9528 recall, 0.9835 specificity, 0.9678 F1-Score and 0.1373 loss. Conclusion Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.

Correction: Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID‑19 Diseases from CT Scans Images

Correction: Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID‑19 Diseases from CT Scans Images

Measurement of Hydraulic Fracture Aperture by Electromagnetic Induction.

We present a new method for accurately measuring the aperture of a fluid-driven fracture. This method uses an eddy current probe located within a completion tool specifically designed to obtain the fracture aperture in the wellbore at the location where the fluid is injected into the fracture. The probe induces an eddy current in a target object, producing a magnetic field that affects the overall magnetic field. It does not have any limitations with respect to fluid pressure and temperature within a large range, making it unlike other methods. We demonstrate the accuracy and performance of the sensor under laboratory conditions. A hydraulic fracture experiment in a porous sandstone is conducted and discussed. The obtained measurement of the evolution of the fracture inlet aperture by the eddy current probe during the multiple injection cycles performed provided robust information. The residual fracture aperture (after the test) measured by the probe is in line with estimations from image processing of X-ray CT scan images as well as a thin-section analysis of sub-parts of the fractured specimen. The robustness and accuracy of this electromagnetic induction probe demonstrated herein under laboratory conditions indicate an interesting potential for field deployment.

Comprehensive Approach to TB Diagnosis Powered by Machine Learning in Endemic Countries

Abstract Introduction/Objective Tuberculosis (TB), a leading cause of mortality worldwide, is primarily diagnosed through methods that face numerous challenges, including lengthy result times and diagnostic inaccuracy, particularly in the context of drug-resistant strains and latent infections. This study proposes a novel approach that integrates multiple diagnostic modalities with machine learning to enhance TB diagnosis accuracy. Methods/Case Report The Machine learning model was tailored for the classification of radiological images, specifically CT scans, using original DICOM images to preserve valuable voxel information. The study involved a curated dataset of CT scans from Pakistan, TB-positive (smear+/Culture+ n=72; smear+/culture- n= 3; smear-/Culture+ n=16; smear-/Culture- n=14; clinical and radiological diagnosis only n= 12) and healthy (n=103) scans from COVID-CTSet were included in the training to assess the specificity of the model. Data preprocessing included noise reduction, histogram matching, and data augmentation techniques to ensure a robust training dataset. The methodology also incorporated using k-fold cross validation and a custom learning rate scheduling strategy to optimize the model’s training process. To further test its robustness, the model was evaluated by swapping random patches and analyzing 60 mm patches from the entire CT scan for local feature categorization. Results (if a Case Study enter NA) The model has demonstrated outstanding performance, with sensitivity and specificity exceeding 95% on our internal dataset (n=117) and aligning well with clinical, smear, culture, and anti-TB antibody findings. It also showed promising results on the external NIH TB Portal’s dataset (n= 1,460). The model is robust to variations in CT scanner image quality and noise profiles, maintaining strong generalization across datasets. Even when analyzing random 60mm patches, effectively classifying all local features Conclusion Our study enhances tuberculosis diagnostic methods in endemic regions by blending machine learning with conventional diagnostic techniques. This approach improves TB detection, particularly the early and accurate identification in areas impacted by drug-resistant strains and asymptomatic latent infections. By integrating radiological and clinico-pathological data, we aim to develop a comprehensive diagnostic score. Moving forward, we plan to further integrate this method into clinical decision support systems and adapt the approach to other infectious diseases.

Analysis of Failed Posterior Fossa Decompression and an Effective Revision Surgery in Patients with Basilar Invagination and Atlantoaxial Dislocation.

The objective of this study was to analyze failed posterior fossa decompression (PFD) in patients with basilar invagination and atlantoaxial dislocation (BI-AAD). Revision surgery in these patients is challenging and has been rarely reported. In addition, the anatomical variations of the vertebral artery increase the risk of revision surgery. Here, we introduce the implementation of a new type of one-stage posterior revision surgery, whose difficulties and effects are summarized. A total of 21 patients with BI-AAD who underwent PFD were retrospectively analyzed in our center from November 2017 to April 2021. The revision surgery in all patients was performed through the posterior approach. The Japanese Orthopaedic Association (JOA) score and the Short Term 12 (SF-12) score were employed to evaluate the clinical symptoms and health status. The distance from the tip of the odontoid to Chamberlain's line (DCL), the atlantodental interval (ADI), the clivus-canal angle (CCA), the diameter of the subarachnoid space (DSS), and the craniovertebral junction triangular area (CTA) were assessed radiographically. The pre- and postoperative results were compared by paired t test. The data of 21 consecutive patients were reviewed, with an average follow-up period of 28 ± 14 months. Postoperative imaging showed effectively reduced compression of BI-AAD. No implant failure or neurovascular injury occurred. Eleven patients had vertebral artery abnormalities, but none had vertebral artery injury. All patients had evidence of bone fusion on the CT scan images within a 12-month follow-up period. The JOA and SF-12 scores were significantly improved 1 year postoperatively (p < 0.001). Posterior surgery using the technique of interarticular distraction, fusion with cage grafting, and fixation is a safe and effective revision surgery to treat patients with basilar invagination and atlantoaxial dislocation who failed PFD, which will result in good outcome.

Determining the Optimal Morphometry of Pedicle Screws in Lumbar and Thoracic Spine in the Iranian Population

Background: The improper length and direction of pedicle screw may also cause it to penetrate the adjacent organs. Unknowing the safe zone of dimensions and directions of the pedicles is essential to proper screw placement. Methods: The present study is a cross-sectional morphometric study on patients with spinal trauma referred to the neurosurgical department in 2021. The MARCO PACS software scale measured the CT scan images and included the vertebral left pedicle diameter, pedicle body distance, body aorta distance, pedicle diameter, height, and area. The entry point and direction of the pedicle screw were made with the “free-hand” method. Results: 301 patients were included, consisting of 113 females (37.5%) and 188 males (62.5%). The mean age of the participants was 42.87±12.3 years. “Among all T12 vertebra parameters, just body aorta distance was not significant between genders (p-value=0.311). The pedicle height and body aorta distance were lowest among 18-35 years patients (p-value=0.001) (p-value=0.003), respectively. Post-hoc analysis revealed a significant difference between 18-35 and 51-70 years for body aorta distance in the L1 vertebra (p-value=0.002). Conclusion: The present investigation has shown significant differences between genders for optimal left pedicle screw parameters, which should be considered before surgery.

Multifractal Analysis in Age-Based Classification for COVID-19 Patients’ CT-Scan Images with Different Noise Levels

Recently, many number of people various countries contracted the COVID-19 (C-19) disease. The identification of this harmful disease and other pneumonia diseases is an important process in the medical world. CT-Scan is mainly prescribed to predict the severity of lung diseases affected by C-19 and other pneumonia diseases. Generally, a CT-Scan image is represented as a grayscale image (GI). Biomedical-oriented GIs are more complex and they are too difficult to recognize the status of lung diseases directly from the experimental images. To address this type of medical problem, a multifractal approach can be applied to analyze and illustrate the GIs in detail. Therefore, the multifractal dimensional analysis is used to diagnose and explore the vehemence of the contamination levels in the lungs. In this study, the complexity of CT-Scan images of C-19 patients is analyzed in order to perform a comparison in terms of age and noise levels. It was also discovered that the intricate of CT-Scan images is considerably varied for patients aged 50 and above when compared to younger subjects. This comparative study indicates that the deadly virus affects elderly persons compared to youngsters. The proposed age-based classification is supported by image processing techniques, qualitative measures, and statistical tools. The obtained results are demonstrated graphically by Generalized Fractal Dimensions (GFD) spectrum, 3D visualization, ANOVA table, and box plots to expose the rate of discrimination of complexity in CT-Scan lung images.

Estimation and experiment on acoustic properties of rice straw (estimation of sound absorption coefficient based on CT image)

The sound absorption characteristics of rice straw were estimated using micro-CT scan images and theoretical analysis. Since it is difficult to express the structure and dimensions of rice straw in a regular manner, the voids in the straw were approximated as clearance between two parallel planes based on the micro-CT scan images. Using the surface area of the straw and the volume of the voids, a theoretical estimation of the sound absorption coefficient of the straw was made. Propagation constants, characteristic impedance, and normal incident sound absorption coefficient were obtained for the derived clearance. Since the rice straw is tubular, the sound absorption coefficient was also obtained assuming that an arbitrary CT scan image is continuous in the thickness direction. In addition, the sound absorption coefficient was estimated from ordinary photographs taken of the sample incident surface. In the experiments, the sound absorption coefficient was measured using a two-microphone impedance tube. The theoretical values were close to the experimental value.

FocalNeXt: A ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images

Early and accurate diagnosis of lung cancer, a life-threatening disease, is critical to the successful treatment of patients with the disease. On the other hand, it is well known that the integration of computer-aided diagnosis (CAD) systems into the diagnostic workflow facilitates the process and improves the diagnostic results of automated systems by reducing the difficulties associated with human observation. In this paper, we present FocalNeXt, a new ConvNeXt-augmented FocalNet architecture specifically designed for automated lung cancer detection from computed tomography (CT) scan images. By combining the strong attention mechanism of FocalNet and the feature extraction mechanism of ConvNeXt within the vision transformer paradigm, FocalNeXt aims to improve diagnostic accuracy. Our comprehensive evaluation of a publicly available Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) CT-scan dataset includes rigorous comparisons and an ablation study. FocalNeXt achieved an accuracy of 99.81%, outperforming the state-of-the-art methods. The model also excelled in sensitivity (99.78%), recall (99.36%), and F1-score (99.56%), positioning FocalNeXt as a leading model for lung cancer detection. The ablation study further demonstrated its efficacy and underscored the robustness of FocalNeXt in different configurations. The results underline its potential to contribute to advances in medical imaging and personalized healthcare.

Fibre waviness reduction in thermoplastic pultrusion by using DREF yarns

Non-reactive thermoplastic pultrusion impregnation issues are mitigated by using hybrid input materials. Co-wound (CW) and commingled yarns are an assembly of continuous polymer and reinforcement fibres. Continuous thermoplastic fibres have shown to induce waviness in the reinforcement fibres during pultrusion due to their shrinkage at high temperature. DREF yarns are composed of a core of continuous reinforcement fibres onto which discontinuous polymer fibres are applied using the friction spinning process. This study, based on the application of 3N and 0N tension on CW and DREF yarns, aimed to highlight the contribution of discontinuous polymer fibres on reducing reinforcement waviness in pultruded rods. CW yarns’ reaction to heating showed continuous polyethylene terephthalate (PET) fibres shrinkage resulting in wavy glass fibres (GF). Conversely, the GF in DREF yarns remained straight. Pultrusion experiments with yarn tension of 3N were done to alleviate the GF waviness. However, the porosity was rather high at 4.2% for CW rods and 2.3% for DREF rods. Pultrusion experiments without tension showed lower porosity of level of 2.9% for CW yarns and as low as 1.1% for DREF yarns. However, CT-scan image indicated GF waviness in CW rods. GF in DREF rods remained straight. The in-plane shear strength reached 119 MPa. Thermoplastic pultrusion using DREF yarns resulted in composites without reinforcement fibre waviness, lower porosity level and superior shear strength.

Impact of scapula anatomical landmark positioning on scapular orientation using CT-based 3D models: an intra-observer repeatability and inter-observer reproducibility study

HypothesisThe primary objective of this study was to assess intra-observer repeatability and inter-observer reproducibility of the 3D coordinates of a set of scapula anatomical landmarks obtained by manual positioning on conventional supine CT-scan based scapula surface models. The secondary objective was to assess intra-observer repeatability and inter-observer reproducibility of the resulting 3D scapular orientation. It was hypothesized that the 3D scapular orientation reliability would be better or similar than established reliability of intra-operative baseplate positioning (i.e. version and inclination) in reverse total shoulder arthroplasty (rTSA). MethodsThree anatomical landmarks, i.e. acromial angle (AA), inferior angle (IA), and trigonum scapulae (TS), were manually positioned on 81 scapula surface models using updated landmark definitions. These models were obtained by the segmentation of CT-scan images acquired from patients undergoing elective rTSA procedures at the Department of Surgery at Geneva University Hospitals between May 2022 and December 2023. This procedure was repeated 3 times per scapula by 3 independent observers. A set of parameters corresponding to the 3D landmark coordinates (expressed in an average scapula coordinate system) and the 3D scapular orientation (expressed as three angles related to the retraction/protraction, lateral/medial rotation, and internal/external rotation) were computed. Intra-observer repeatability and inter-observer reproducibility were assessed for each of these parameters using an intra-class correlation as a relative reliability metric. Standard error of measurement (SEM) was also reported as an absolute reliability metric. ResultsIntra-observer ICC and inter-observer ICC ranged from poor to moderate for AA, poor to excellent for IA, and poor to excellent for TS. However, low SEM, ranging from 0.4 mm to 2.4 mm for intra-observer repeatability and from 0.2 mm to 1.8 mm for inter-observer reproducibility, were obtained for every coordinate of each anatomical landmark. This results in poor to moderate intra-observer ICC and inter-observer ICC for scapula angular orientation. Again, low SEM, ranging from 0.4° to 0.8° for intra-observer repeatability and 0.4° to 0.6° for inter-observer reproducibility, were obtained, arguing for reliable measurements. Discussion and ConclusionThis study demonstrates that manual positioning of the three scapula anatomical landmarks recommended by the International Society of Biomechanics can be performed in a reliable manner across measures and observers on surface bone models obtained from CT-scan images. However, a clear and standardized definition of these landmarks is needed to ensure consistency across measurements.