Search results for: multidetector computed tomography

Authors: Sidi M. A. Ghaly, Mohammad O. Khan, Mohammed Shalaby, Khaled A. Al-Snaie

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Electrical capacitance tomography (ECT) is a valuable, non-invasive technique used to monitor multiphase flow processes, especially within industrial pipelines. This study focuses on the design, testing, and performance comparison of ECT sensors configured with 8, 12, and 16 electrodes, aiming to evaluate their effectiveness in imaging accuracy, resolution, and sensitivity. Each sensor configuration was designed to capture the spatial permittivity distribution within a pipeline cross-section, enabling visualization of phase distribution and flow characteristics such as oil and water interactions. The sensor designs were implemented and tested in closed pipes to assess their response to varying flow regimes. Capacitance data collected from each electrode configuration were reconstructed into cross-sectional images, enabling a comparison of image resolution, noise levels, and computational demands. Results indicate that the 16-electrode configuration yields higher image resolution and sensitivity to phase boundaries compared to the 8- and 12-electrode setups, making it more suitable for complex flow visualization. However, the 8 and 12-electrode sensors demonstrated advantages in processing speed and lower computational requirements. This comparative analysis provides critical insights into optimizing ECT sensor design based on specific industrial requirements, from high-resolution imaging to real-time monitoring needs.

Keywords: capacitance tomography, modeling, simulation, electrode, permittivity, fluid dynamics, imaging sensitivity measurement

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Authors: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Dominik Panek, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa łucja Stępień, Faranak Tayefi, Paweł Moskal

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This paper develops the transformation of non-image data into 2-dimensional matrices, as a preparation stage for classification based on convolutional neural networks (CNNs). In positron emission tomography (PET) studies, CNN may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, much PET data still exists in non-image format and this fact opens a question on whether they can be used for training CNN. In this contribution, the main focus of this paper is the problem of processing vectors with a small number of features in comparison to the number of pixels in the output images. The proposed methodology was applied to the classification of PET coincidence events.

Keywords: convolutional neural network, kernel principal component analysis, medical imaging, positron emission tomography

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Authors: Boonyalitpun P., Pruckpattranon P., Thonghom A., Rotpenpian N.

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Osteoarthritis is a musculoskeletal and neuromuscular abnormality, masticatory muscle, and other tissue that causes pain and breaks down the articular surface of the temporomandibular joint (TMJ). The aim of this study is to investigate the change in the mandibular condyle, in terms of thickness and porosity, and osteoclast marker in the mandibular condyle of TMJ induced osteoarthritis mice (TMJ-OA mice). We investigated the bony changes in the TMJ structure of a complete Freund adjuvant (CFA)-injected TMJ in a mice model over 28 days. On day 28, we observed any change in the TMJ by a micro computed tomography scan (micro-CT scan) in the parameters of trabecular microarchitecture. Then we studied the thickness of the condyles by hematoxylin and eosin staining. Moreover, we calculated the area around the TMJ’s condylar head containing the osteoclast expression by TRAP (Tartrate-resistant acid phosphatase) immunohistochemistry staining. The result found that the parameter of a micro-CT scan was no different from microarchitecture in the TMJ compared with the control group; however, mandibular condyles of the TMJ-OA group was significantly thinner than the control groups, and the osteoclast expression significantly increased in the TMJ-OA group. Therefore, our findings suggest that CFA-induced TMJ-OA represents an expression of osteoclast mandibular condyle of the TMJ, which is the proposed mechanism for a TMJ-OA model.

Keywords: condyle, osteoarthritis, osteoclast, temporomandibular joint

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Authors: Ana Beatriz C. G. Silva, Luiz Carlos Wrobel, Fernando Luiz B. Ribeiro

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This paper presents a thermoregulation model using the finite element method to perform numerical analyses of brain cooling procedures as a contribution to the investigation on the use of therapeutic hypothermia after ischemia in adults. The use of computational methods can aid clinicians to observe body temperature using different cooling methods without the need of invasive techniques, and can thus be a valuable tool to assist clinical trials simulating different cooling options that can be used for treatment. In this work, we developed a FEM package applied to the solution of the continuum bioheat Pennes equation. Blood temperature changes were considered using a blood pool approach and a lumped analysis for intravascular catheter method of blood cooling. Some analyses are performed using a three-dimensional mesh based on a complex geometry obtained from computed tomography medical images, considering a cooling blanket and a intravascular catheter. A comparison is made between the results obtained and the effects of each case in brain temperature reduction in a required time, maintenance of body temperature at moderate hypothermia levels and gradual rewarming.

Keywords: brain cooling, finite element method, hypothermia treatment, thermoregulation

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Authors: Vincent Kheswa

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X-rays are ionizing electromagnetic radiation that is used in various industries such as computed tomography scans, dental X-rays, and screening freight trains. However, they pose health risks to humans if they are not shielded properly. In recent years, many researchers around the globe have been searching for nontoxic best possible glass materials for shielding X-rays. In this work, the x-ray shielding properties of 45Na₂O + 10 Bi₂O₃ + (5 - x)TiO₂+ (x) Nb₂O₅ + 40 P₂O₅, were x = 0, 1, 3, 5 mol%, glass materials were studied. The results revealed that the glass sample with the highest TiO2 content has the highest mass and linear attenuation coefficients and lowest half-value thickness, tenth-value thickness and mean-free path in the 20 to 80 keV energy region. The sample with 3 mol% of Nb₂O₅ has the highest mass and linear attenuation coefficients and the lowest half-value thickness, tenth-value thickness, and mean-free path at 15 keV and photon energies between 80 to 300 keV. It was, therefore, concluded that 45Na₂O + 10 Bi₂O₃ + 5 TiO₂ + 40 P₂O₅ glass is best for shielding x-rays of energies between 20 and 80 keV, while 45Na₂O + 10 Bi₂O₃ + 2 TiO₂ + 3 Nb₂O₅ + 40 P₂O₅ is best for shielding 15 keV x-rays and x-rays of energies between 80 keV and 300 keV.

Keywords: bismuth-titanium-phosphate glass, x-ray shielding, LAC, MAC, radiation shielding

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Authors: Radhe Tado, Ashish B. Deoghare, K. M. Pandey

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The aim of this study is to estimate the effect of blood flow through the coronary artery in human heart so as to assess the coronary artery disease.Velocity, wall shear stress (WSS), strain rate and wall pressure distribution are some of the important hemodynamic parameters that are non-invasively assessed with computational fluid dynamics (CFD). These parameters are used to identify the mechanical factors responsible for the plaque progression and/or rupture in left coronary arteries (LCA) in coronary arteries.The initial step for CFD simulations was the construction of a geometrical model of the LCA. Patient specific artery model is constructed using computed tomography (CT) scan data with the help of MIMICS Research 19.0. For CFD analysis ANSYS FLUENT-14.5 is used.Hemodynamic parameters were quantified and flow patterns were visualized both in the absence and presence of coronary plaques. The wall pressure continuously decreased towards distal segments and showed pressure drops in stenotic segments. Areas of high WSS and high flow velocities were found adjacent to plaques deposition.

Keywords: angiography, computational fluid dynamics (CFD), time-average wall shear stress (TAWSS), wall pressure, wall shear stress (WSS)

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Authors: Ibrahim A. Elsayed, Mohamed H. Elalfy, Mostafa M. Abdalla

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A strain-based failure criterion for composite notched laminates is introduced where the most critical stress concentration factor for the anisotropic notched laminates could be related to the failure of the corresponding quasi-isotropic laminate and the anisotropy ratio of the laminate. The proposed criterion will simplify the design of composites to meet notched failure requirements by eliminating the need for the detailed specifications of the stacking sequence at the preliminary design stage. The designer will be able to design based on the stiffness of the laminate, then at a later stage, select an appropriate stacking sequence to meet the stiffness requirements. The failure strains for the notched laminates are computed using the material’s Omni-strain envelope. The concept of Omni-strain envelope concerns the region of average strain where the laminate is safe regardless of ply orientation. In this work, we use Hashin’s failure criteria and the strains around the hole are computed using Savin’s analytic solution. A progressive damage analysis study has been conducted where the failure loads for the notched laminates are computed using finite element analysis. The failure strains are computed and used to estimate the concentration factor. It is found that the correlation found using Savin’s analytic solution predicts the same ratio of concentration factors between anisotropic and quasi-isotropic laminates as the more expensive progressive failure analysis.

Keywords: anisotropy ratio, failure criteria, notched laminates, Omni-strain envelope, savin’s solution

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Perumalla Naveen Kumar

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Global Positioning System (GPS) is used for civilian and military user positioning applications. The accuracy of GPS is degrading mainly because of ionospheric error. It is very important to analyze the effects of ionosphere on the performance of satellite systems especially in the low latitude regions. These variations depend on the Total Electron Content (TEC) in the ionosphere. To investigate the variations in the atmosphere, a mini satellite known as YOUTHSAT is launched by India. This is the outcome of the collaboration between India and USSR. One of the YOUTHSAT Indian payload is RaBIT (Radio Beacon for Ionospheric Tomography). In this paper, YOUTHSAT RaBIT payload data for the three typical days of 2011 are considered. The analysis is carried out for four Indian stations. The variations of Slant TEC, elevation angle and azimuth angles are analyzed with respect to local time. The obtained results are encouraging.

Keywords: Global Positioning System (GPS), Total Electron Content (TEC), YOUTHSAT, Radio Beacon for Ionospheric Tomography (RaBIT)

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Authors: Amir Peyman Soleymani, Jasna Jankovic

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The augmented demand of the clean and renewable energy has necessitated the fuel cell and battery industries to produce more efficient devices at the lower prices, which can be achieved through the improvement of the electrode. Microstructural characterization, as one of the main materials development tools, plays a pivotal role in the production of better clean energy devices. In this study, methods for characterization and studying of the defects and components distribution were performed on the polymer electrolyte membrane fuel cell (PEMFC) and Li-ion battery (LIB) electrodes in 2D and 3D. The particles distribution, porosity, mechanical defects, and component distribution were studied by Scanning Electron Microscope (SEM), SEM-Focused Ion Beam (SEM-FIB), and Scanning Transmission Electron Microscope equipped with Energy Dispersive Spectroscopy (STEM-EDS). The 3D results obtained from X-ray Computed Tomography (XCT) revealed the pathways for electron and ion conductivity and defects progression maps. Computer-aided methods (Avizo) were employed to simulate the properties and performance of the microstructure in the electrodes. The suggestions were provided to improve the performance of PEMFCs and LIBs by adjusting the microstructure and the distribution of the components in the electrodes.

Keywords: PEM fuel cells, Li-ion batteries, 2D and 3D imaging, materials characterizations

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Authors: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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Authors: Yousif M. Makeen

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This research will address the processes of geochemical characterization and hydrocarbon generation process occurring within hydrocarbon source and/or reservoir rocks. The geochemical characterization includes organic-inorganic associations that influence the storage capacity of unconventional hydrocarbon resources (e.g. shale gas) and the migration process of oil/gas of the petroleum source/reservoir rocks. Kerogen i.e. the precursor of petroleum, occurs in various forms and types, may either be oil-prone, gas-prone, or both. China has a number of petroleum-bearing sedimentary basins commonly associated with shale gas, oil sands, and oil shale. Taken Sichuan basin as a selected basin in this study, the Sichuan basin has recorded notable successful discoveries of shale gas especially in the marine shale reservoirs within the area. However, a notable discoveries of lacustrine shale in the North-Este Fuling area indicate the accumulation of shale gas within non-marine source rock. The objective of this study is to evaluate the hydrocarbon storage capacity, generation, and retention processes in the rock matrix of hydrocarbon source/reservoir rocks within the Sichuan basin using an advanced X-ray tomography 3D imaging computational technology, commonly referred to as Micro-CT, SEM (Scanning Electron Microscope), optical microscope as well as organic geochemical facilities (e.g. vitrinite reflectance and UV light). The preliminary results of this study show that the lacustrine shales under investigation are acting as both source and reservoir rocks, which are characterized by very fine grains and very low permeability and porosity. Three pore structures have also been characterized in the study in the lacustrine shales, including organic matter pores, interparticle pores and intraparticle pores using x-ray Computed Tomography (CT). The benefits of this study would be a more successful oil and gas exploration and higher recovery factor, thus having a direct economic impact on China and the surrounding region. Methodologies: SRA TOC/TPH or Rock-Eval technique will be used to determine the source rock richness (S1 and S2) and Tmax. TOC analysis will be carried out using a multi N/C 3100 analyzer. The SRA and TOC results were used in calculating other parameters such as hydrogen index (HI) and production index (PI). This analysis will indicate the quantity of the organic matter. Minimum TOC limits generally accepted as essential for a source-rock are 0.5% for shales and 0.2% for carbonates. Contributions: This research could solve issues related to oil potential, provide targets, and serve as a pathfinder to future exploration activity in the Sichuan basin.

Keywords: shale gas, unconventional resources, organic chemistry, Sichuan basin

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Authors: Vineel Inampudi, Arjun Prakash, Joseph Vinod

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Aims and Objectives: To evaluate the interrater reliability in grading ASPECTS score, between emergency medicine physician at first contact and radiologist among patients with acute ischemic stroke. Materials and Methods: We conducted a retrospective analysis of 86 acute ischemic stroke cases referred to the Department of Radiodiagnosis during November 2014 to January 2016. The imaging (plain CT scan) was performed using GE Bright Speed Elite 16 Slice CT Scanner. ASPECTS score was calculated separately by an emergency medicine physician and radiologist. Interrater reliability for total and dichotomized ASPECTS (≥ 6 and < 6) scores were assessed using statistical analysis (ICC and Cohen ĸ coefficients) on SPSS software (v17.0). Results: Interrater agreement for total and dichotomized ASPECTS was substantial (ICC 0.79 and Cohen ĸ 0.68) between the emergency physician and radiologist. Mean difference in ASPECTS between the two readers was only 0.15 with standard deviation of 1.58. No proportionality bias was detected. Bland Altman plot was constructed to demonstrate the distribution of ASPECT differences between the two readers. Conclusion: Substantial interrater agreement was noted in grading ASPECTS between emergency medicine physician at first contact and radiologist thereby confirming its robustness even in a rural setting.

Keywords: ASPECTS, computed tomography, MCA territory, stroke

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Authors: Khaled Soliman, Abdullah Alrushoud, Abdulrahman Alkhalifah, Raed Albathi, Salman Altymiat

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Radiation dose received by patients undergoing Computed Tomography (CT) examination of the cervical spine was evaluated using Gafchromic XR-QA2 films and CT-EXPO software (ver. 2.3), in order to document our clinical dose values and to compare our results with other benchmarks reported in the current literature. Radiochromic films were recently used as practical dosimetry tool that provides dose profile information not available using the standard ionisation chamber routinely used in CT dosimetry. We have developed an in-house program to use the films in order to calculate the Entrance Dose Length Product (EDLP) in (mGy.cm) and to relate the EDLP to various organ doses calculated using the CT-EXPO software. We also calculated conversion factor in (mSv/mGy.cm) relating the EDLP to the effective dose (ED) from the examination using CT-EXPO software. Variability among different types of CT scanners and dose modulation methods are reported from at least three major CT brands available at our medical institution. Our work describes the dosimetry method and results are reported. The method can be used as in-vivo dosimetry method. But this work only reports results obtained from adult female anthropomorphic Phantom studies.

Keywords: CT dosimetry, gafchromic films, XR-QA2, CT-Expo software

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Authors: Veronica Sorgato, Jeremy Belhassen, Philippe Chartier, Roddy Sihanath, Nicolas Docquiere, Jean-Yves Giraud

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We analyzed the image-guided radiotherapy method used by the TomoTherapy® System (Accuray Corp.) for patient repositioning in clinical routine. The TomoTherapy® System computes X, Y, Z and roll displacements to match the reference CT, on which the dosimetry has been performed, with the pre-treatment MV CT. The accuracy of the repositioning method has been studied according to the treatment localization. For this, a database of 18774 treatment sessions, performed during 2 consecutive years (2016-2017 period) has been used. The database includes the X, Y, Z and roll displacements proposed by TomoTherapy® System as well as the manual correction of these proposals applied by the radiation therapist. This manual correction aims to further improve the repositioning based on the clinical situation and depends on the structures surrounding the target tumor tissue. The statistical analysis performed on the database aims to define repositioning limits to be used as security and guiding tool for the manual adjustment implemented by the radiation therapist. This tool will participate not only to notify potential repositioning errors but also to further improve patient positioning for optimal treatment.

Keywords: accuracy, IGRT MVCT, image-guided radiotherapy megavoltage computed tomography, statistical analysis, tomotherapy, localization

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Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

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Authors: Eric Yi-Hsiu Huang, Meng-Chun Kao, Wen-Chuan Kuo

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For every laparoscopic surgery, a safe pneumoperitoneumcreation (gaining access to the peritoneal cavity) is the first and essential step. However, closed pneumoperitoneum is usually obtained by blind insertion of a Veress needle into the peritoneal cavity, which may carry potential risks suchas bowel and vascular injury.Until now, there remains no definite measure to visually confirm the position of the needle tip inside the peritoneal cavity. Therefore, this study established an image-guided Veress needle method by combining a fiber probe with optical coherence tomography (OCT). An algorithm was also proposed for determining the exact location of the needle tip through the acquisition of OCT images. Our method not only generates a series of “live” two-dimensional (2D) images during the needle puncture toward the peritoneal cavity but also can eliminate operator variation in image judgment, thus improving peritoneal access safety. This study was approved by the Ethics Committee of Taipei Veterans General Hospital (Taipei VGH IACUC 2020-144). A total of 2400 in vivo OCT images, independent of each other, were acquired from experiments of forty peritoneal punctures on two piglets. Characteristic OCT image patterns could be observed during the puncturing process. The ROC curve demonstrates the discrimination capability of these quantitative image features of the classifier, showing the accuracy of the classifier for determining the inside vs. outside of the peritoneal was 98% (AUC=0.98). In summary, the present study demonstrates the ability of the combination of our proposed automatic identification method and OCT imaging for automatically and objectively identifying the location of the needle tip. OCT images translate the blind closed technique of peritoneal access into a visualized procedure, thus improving peritoneal access safety.

Keywords: pneumoperitoneum, optical coherence tomography, automatic identification, veress needle

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Authors: Paola Causin, Andrea Aspri, Alessandro Benfenati

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Diffuse Optical Tomography (DOT) is an emergent medical imaging technique which employs NIR light to estimate the spatial distribution of optical coefficients in biological tissues for diagnostic purposes, in a noninvasive and non-ionizing manner. DOT reconstruction is a severely ill-conditioned problem due to prevalent scattering of light in the tissue. In this contribution, we present our research in adopting hybrid knowledgedriven/data-driven approaches which exploit the existence of well assessed physical models and build upon them neural networks integrating the availability of data. Namely, since in this context regularization procedures are mandatory to obtain a reasonable reconstruction [1], we explore the use of neural networks as tools to include prior information on the solution. 2. Materials and Methods The idea underlying our approach is to leverage neural networks to solve PDE-constrained inverse problems of the form 𝒒 ∗ = 𝒂𝒓𝒈 𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃), (1) where D is a loss function which typically contains a discrepancy measure (or data fidelity) term plus other possible ad-hoc designed terms enforcing specific constraints. In the context of inverse problems like (1), one seeks the optimal set of physical parameters q, given the set of observations y. Moreover, 𝑦̃ is the computable approximation of y, which may be as well obtained from a neural network but also in a classic way via the resolution of a PDE with given input coefficients (forward problem, Fig.1 box ). Due to the severe ill conditioning of the reconstruction problem, we adopt a two-fold approach: i) we restrict the solutions (optical coefficients) to lie in a lower-dimensional subspace generated by auto-decoder type networks. This procedure forms priors of the solution (Fig.1 box ); ii) we use regularization procedures of type 𝒒̂ ∗ = 𝒂𝒓𝒈𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃)+ 𝑹(𝒒), where 𝑹(𝒒) is a regularization functional depending on regularization parameters which can be fixed a-priori or learned via a neural network in a data-driven modality. To further improve the generalizability of the proposed framework, we also infuse physics knowledge via soft penalty constraints (Fig.1 box ) in the overall optimization procedure (Fig.1 box ). 3. Discussion and Conclusion DOT reconstruction is severely hindered by ill-conditioning. The combined use of data-driven and knowledgedriven elements is beneficial and allows to obtain improved results, especially with a restricted dataset and in presence of variable sources of noise.

Keywords: inverse problem in tomography, deep learning, diffuse optical tomography, regularization

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

Abstract:

The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Atilla Bayram

Abstract:

This paper presents the trajectory tracking control of a spatial redundant hybrid manipulator. This manipulator consists of two parallel manipulators which are a variable geometry truss (VGT) module. In fact, each VGT module with 3-degress of freedom (DOF) is a planar parallel manipulator and their operational planes of these VGT modules are arranged to be orthogonal to each other. Also, the manipulator contains a twist motion part attached to the top of the second VGT module to supply the missing orientation of the endeffector. These three modules constitute totally 7-DOF hybrid (parallel-parallel) redundant spatial manipulator. The forward kinematics equations of this manipulator are obtained, then, according to these equations, the inverse kinematics is solved based on an optimization with the joint limit avoidance. The dynamic equations are formed by using virtual work method. In order to test the performance of the redundant manipulator and the controllers presented, two different desired trajectories are followed by using the computed force control method and a switching control method. The switching control method is combined with the computed force control method and genetic algorithm. In the switching control method, the genetic algorithm is only used for fine tuning in the compensation of the trajectory tracking errors.

Keywords: computed force method, genetic algorithm, hybrid manipulator, inverse kinematics of redundant manipulators, variable geometry truss

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Authors: Luciano Garelli, Marco Schauer, Jorge D’Elia, Mario A. Storti, Sabine C. Langer

Abstract:

This paper discuss a coupling strategy of two different software packages to provide fluid structure interaction (FSI) analysis. The basic idea is to combine the advantages of the two codes to create a powerful FSI solver for two and three dimensional analysis. The fluid part is computed by a program called PETSc-FEM, a software developed at Centro de Investigación de Métodos Computacionales (CIMEC). The structural part of the coupled process is computed by the research code elementary Parallel Solver (elPaSo) of the Technische Universität Braunschweig, Institut für Konstruktionstechnik (IK).

Keywords: computational fluid dynamics (CFD), fluid structure interaction (FSI), finite element method (FEM), software

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Authors: Parisa Mansour

Abstract:

The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Fatemeh Sadeghi, Peyman Sheikhzadeh

Abstract:

Introduction: Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm was recently developed to suppress excessive noise by applying a relative difference penalty. The aim of this study was to investigate the effect of various strengths of noise penalization factor in the BSREM algorithm under different acquisition duration and lesion sizes in order to determine an optimum penalty factor by considering both quantitative and qualitative image evaluation parameters in clinical uses. Materials and Methods: The NEMA IQ phantom and 15 clinical whole-body patients with prostate cancer were evaluated. Phantom and patients were injected withGallium-68 Prostate-Specific Membrane Antigen(68 Ga-PSMA)and scanned on a non-time-of-flight Discovery IQ Positron Emission Tomography/Computed Tomography(PET/CT) scanner with BGO crystals. The data were reconstructed using BSREM with a β-value of 100-500 at an interval of 100. These reconstructions were compared to OSEM as a widely used reconstruction algorithm. Following the standard NEMA measurement procedure, background variability (BV), recovery coefficient (RC), contrast recovery (CR) and residual lung error (LE) from phantom data and signal-to-noise ratio (SNR), signal-to-background ratio (SBR) and tumor SUV from clinical data were measured. Qualitative features of clinical images visually were ranked by one nuclear medicine expert. Results: The β-value acts as a noise suppression factor, so BSREM showed a decreasing image noise with an increasing β-value. BSREM, with a β-value of 400 at a decreased acquisition duration (2 min/ bp), made an approximately equal noise level with OSEM at an increased acquisition duration (5 min/ bp). For the β-value of 400 at 2 min/bp duration, SNR increased by 43.7%, and LE decreased by 62%, compared with OSEM at a 5 min/bp duration. In both phantom and clinical data, an increase in the β-value is translated into a decrease in SUV. The lowest level of SUV and noise were reached with the highest β-value (β=500), resulting in the highest SNR and lowest SBR due to the greater noise reduction than SUV reduction at the highest β-value. In compression of BSREM with different β-values, the relative difference in the quantitative parameters was generally larger for smaller lesions. As the β-value decreased from 500 to 100, the increase in CR was 160.2% for the smallest sphere (10mm) and 12.6% for the largest sphere (37mm), and the trend was similar for SNR (-58.4% and -20.5%, respectively). BSREM visually was ranked more than OSEM in all Qualitative features. Conclusions: The BSREM algorithm using more iteration numbers leads to more quantitative accuracy without excessive noise, which translates into higher overall image quality and lesion detectability. This improvement can be used to shorter acquisition time.

Keywords: BSREM reconstruction, PET/CT imaging, noise penalization, quantification accuracy

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Authors: Johannes Keil, Eric Mischorr, Veit Würfel, Jan Condé-Wolter, Alexander Liebsch, Maik Gude

Abstract:

In the aviation sector wastewater pipes are subjected to many different mechanical and medial loads. Worst-case scenarios include high-velocity impacts resulting from the introduction of foreign objects into the system. The industry is seeking to reduce the weight of these pipes, which are currently manufactured from titanium. A promising alternative is the use of fiber-reinforced polymers (FRP), specifically carbon fiber (CF) reinforced polyaryletherketone (PAEK) laminates. This study employs an experimental methodology to investigate the impact resistance of CF/PAEK laminates, with a particular focus on three configurations: crimp, non-crimp, and interleaved matrix rich films in cross-ply laminates. High-velocity impacts were performed using a gas gun resulting in three-dimensional damage patterns. Afterwards the damage behavior was qualitatively and quantitatively analyzed using ultrasonic scans and computed tomography (CT). Samples with an interleaved matrix-rich film led to a reduction of the damage area by around 40% compared to the non-interleaved, non-crimp samples, while the crimp architecture resulted in a reduction of more than 60%. Therefore, these findings contribute to understanding the influence of laminate architecture on impact resistance, paving the way for more efficient materials in aviation applications.

Keywords: fracture toughness, high-velocity-impact, textile architecture, thermoplastic composites

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Authors: Ariungerel Jargal

Abstract:

: Cave mining is a bulk underground mining method, which allows large low-grade deposits to be mined underground. This method involves undermining the orebody to make it collapse under its own weight into a series of chambers from which the ore extracted. It is a useful technique to extend the life of large deposits previously mined by open pits, and it is a method increasingly proposed for new mines around the world. We plan to conduct a feasibility study using Electrical impedance tomography (EIT) technology to show how much subsidence there is at the intersection with the cave mining surface. EIT is an imaging technique which uses electrical measurements at electrodes attached on the body surface to yield a cross-sectional image of conductivity changes within the object. EIT has been developed in several different applications areas as a simpler, cheaper alternative to many other imaging methods. A low frequency current is injected between pairs of electrodes while voltage measurements are collected at all other electrode pairs. In the difference EIT, images are reconstructed of the change in conductivity distribution (σ) between the acquisition of the two sets of measurements. Image reconstruction in EIT requires the solution of an ill-conditioned nonlinear inverse problem on noisy data, typically requiring make simpler assumptions or regularization. It is noted that the ratio of current to voltage represents a complex value according to Ohm’s law, and that it is theoretically possible to re-express EIT. The results of the experiment were presented on the simulation, and it was concluded that it is possible to conduct further real experiments. Drill a certain number of holes in the top wall of the cave to attach the electrodes, flow a current through them, and measure and acquire the potential through these electrodes. Appropriate values should be selected depending on the distance between the holes, the frequency and duration of the measurements, the surface characteristics and the size of the study area using an EIT device.

Keywords: impedance tomography, cave mining, soil, EIT device

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Authors: Niall Fennessy, Charlotte Yin, Vineet Gorolay, Michael Chan, Ilias Drivas

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Background: The aim of this study was to evaluate the effect of manual aspiration of air from the pleural cavity in mitigating the need for chest drain placement after a CT-guided lung biopsy. Method: This is a single institution retrospective review of CT-guided lung biopsies performed on 799 patients between September 2013 and May 2021 in a major tertiary hospital. Percutaneous manual aspiration of air was performed in 104/306 patients (34%) with pneumothoraxes as a preventative measure. Simple and multivariate analysis was performed to identify independent risk factors (modifiable and nonmodifiable) for the success of manual aspiration in mitigating the need for chest drain insertion. Results: The overall incidence of pneumothorax was 37% (295/799). Chest drains were inserted for 81/295 (27%) of the pneumothoraxes, representing 81/799 (10%) of all CT-guided lung biopsies. Of patients with pneumothoraces, 104 (36%) underwent percutaneous aspiration via either the coaxial guide needle or an 18 or 20G intravenous catheter attached to a three-way stopcock and syringe. Amongst this group, 13 patients (13%) subsequently required chest drain insertion. The success of percutaneous aspiration in avoiding subsequent pleural drain insertion decreased with aspiration volume >500mL, radial pneumothorax depth >3cm, increased subpleural depth of the lesion, and the presence of background emphysema.

Keywords: computed tomography, lung biopsy, pneumothorax, manual aspiration, chest drainage

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Authors: Angelis P. Barlampas

Abstract:

PURPOSE: The purpose of this paper is to demonstrate the important role of taking an appropriate and detailed history, in order to reach the best possible diagnostic conclusion. MATERIAL: A patient presented to the emergency department due to the sudden onset of continuous abdominal pain, during the last hour and with the clinical symptoms of an acute abdomen. During the clinical examination, signs of peritoneal irritation and diffuse abdominal tenderness were found. The rest of the clinical and laboratory tests did not reveal anything important. From the reported medical history, nothing of note was found, except for the report of a large liver cyst, for which he was advised not to take any further action, except from regular ultrasound examination . METHOD: A computed tomography examination was performed after per os administration of gastrografin, which revealed a hyperdense ascitic effusion, similar in density to that of gastrografin within the intestinal tract. The presence of a large cyst of the left hepatic lobe was confirmed, contacting and pushing against the stomach. In the area of the contact between the liver cyst and the pylorus, there were extraluminal air bubbles and local opacity of the peritoneal fat, with a small hyperdense effusion. Result : The above, as well as the absence of a history of stomach ulcer or recent trauma, or other pathology, argue in favor of acute pyloric perforation, due to mural necrosis, in response to chronic external pressure from the pre-existing large liver cyst.

Keywords: perforation, stomach, large liver cyst, CT abdomen, acute abdominal pain, intraperitoneal leakage, constrast leakage

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Authors: Bijaya Kumar Sahoo

Abstract:

The effect of III-V nitride semiconductors on performance of a graphene-on-gold surface plasmon resonance (SPR) biosensor has been investigated theoretically. III-V nitrides (AlN, GaN and InN) have been grown between gold (Au) and graphene layers. The sensitivity and performance of the biosensor have been computed for with and without semiconductors. Due to superior electronic and optical properties, III-V nitrides demonstrate high sensitivity and performance over Si and Ge. The enhancement of evanescent electric field due to III-V nitrides have been computed and found highest for InN. The analysis shows that for a high-sensitive imaging biosensor the required optimal thickness of gold, InN and graphene are respectively 49 nm, 11 nm and 0.34 nm for the light of wavelength =633 nm (red He-Ne laser). This study suggests that InN would be a better choice for fabrication of new imaging SPR biosensors.

Keywords: SPR biosensor, optical properties, III-V nitrides, sensitivity, enhancement of electric field, performance of graphene gold SPR biosensor

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