Search

Recent morphological data on human brain development are quite fragmentary. However, they are highly requested for a number of medical practices, educational programs, and fundamental research in the fields of embryology, cytology and histology, neurology, physiology, path anatomy, neonatology, and others. This paper provides the initial information on the new online Human Prenatal Brain Development Atlas (HBDA). The Atlas will start with forebrain annotated hemisphere maps, based on human fetal brain serial sections at the different stages of prenatal ontogenesis. Spatiotemporal changes in the regional-specific immunophenotype profiles will also be demonstrated on virtual serial sections. The HBDA can serve as a reference database for the neurological research, which provides opportunity to compare the data obtained by noninvasive techniques, such as neurosonography, X-ray computed tomography and magnetic resonance imaging, functional magnetic resonance imaging, 3D high-resolution phase-contrast computed tomography visualization techniques, as well as spatial transcriptomics data. It could also become a database for the qualitative and quantitative analysis of individual variability in the human brain. Systemized data on the mechanisms and pathways of prenatal human glio- and neurogenesis could also contribute to the search for new therapy methods for a large spectrum of neurological pathologies, including neurodegenerative and cancer diseases. The preliminary data are now accessible on the special HBDA website. [ABSTRACT FROM AUTHOR]

Cork spot-like physiological disorder (CSPD) is a newly identified issue in 'Kurenainoyume' apples, yet its mechanism remains unclear. To investigate CSPD, we conducted morphological observations on 'Kurenainoyume' apples with and without pre-harvest fruit-bagging treatment using light-impermeable paper bags. Non-bagged fruit developed CSPD in mid-August, while no CSPD symptoms were observed in bagged fruit. The bagging treatment significantly reduced the proportion of opened lenticels, with only 17.9% in bagged fruit compared to 52.0% in non-bagged fruits. In non-bagged fruit, CSPD spots tended to increase from the lenticels, growing in size during fruit development. The cuticular thickness and cross-sectional area of fresh cells in CSPD spots were approximately 16 µm and 1600 µm², respectively. Healthy non-bagged fruit reached these values around 100 to 115 days after full bloom from mid- to late August. Microscopic and computerized tomography scanning observations revealed that many CSPD spots developed at the tips of vascular bundles. Therefore, CSPD initiation between opened lenticels and vascular bundle tips may be influenced by water stress, which is potentially caused by water loss, leading to cell death and the formation of CSPD spots. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability. [ABSTRACT FROM AUTHOR]

By analyzing the three-dimensional digital model of a real carbon paper gas diffusion layer (GDL) reconstructed by X-ray computed tomography (CT), it was found that fibers are not distributed at any angle but within a certain range. The fiber orientation can be represented by fiber pitch (i.e., the angle between a single fiber and the in-plane direction). The effect of fiber orientation on stochastic reconstruction and transport properties (permeability) was investigated in this paper to find which fiber pitch range can achieve a better GDL on fluid flow. First, the actual fiber pitch was measured by analyzing SGL-24BA images obtained by X-ray CT. Also, seven different ranges of fiber pitch were randomly chosen to reconstruct GDL. Then, the permeability of these digital models was calculated using the Lattice Bolzmann Method (LBM) and discussed to obtain the fiber pitch range of the optimal permeability. The results show that the mean fiber pitch of SGL-24BA is 2.40° and the individual values are all less than 6°, also, the permeability of the through-plane direction increases gradually as the range of fiber pitch increases, which can be used for the structural design of carbon paper GDL. [ABSTRACT FROM AUTHOR]

Historical documents are often severely damaged, making it impossible to open them manually without causing further damage. To address this challenge, computed tomography (CT) has emerged as a non-destructive method to explore the inside in a different way. However, the use of ionising radiation in CT scanning raises concerns about its impact on fragile historical documents. This study presents a methodology that uses a test object to conduct preliminary investigations to evaluate the capability of a CT scanner for digital preservation of historical documents. By assessing the feasibility and determining the setting parameters in advance, the X-ray exposure to historical documents can be minimised. For this purpose, a large dataset of inter-page distances was obtained from CT scans of a specially developed test object. The results obtained show a consistent correlation between the page-to-page distances and the derived modulation depths. This method offers great potential for assessing the separability of the pages of historical documents even before they are exposed to radiation for digitisation. Overall, this study helps to reduce the impact of X-ray radiation on sensitive historical documents during digitisation using CT, with the aim of preserving this fragile cultural heritage for future generations. [ABSTRACT FROM AUTHOR]

The corrosion of steel rebars is a prevalent factor leading to the diminished durability of reinforced concrete structures, posing a significant challenge to the safety of structural engineering. To tackle this issue, extensive research has been conducted, yielding a variety of theoretical insights and remedial measures. This review paper offers an exhaustive analysis of the passivation processes and corrosion mechanisms affecting steel rebars in reinforced concrete. It identifies key factors such as chloride ion penetration and concrete carbonization that primarily influence rebar corrosion. Furthermore, this paper discusses a suite of strategies designed to enhance the longevity of reinforced concrete structures. These include improving the concrete protective layer's quality and bolstering the rebars' corrosion resistance. As corrosion testing is essential for evaluating steel rebars' resistance, this paper also details natural and accelerated corrosion testing methods applicable to rebars in concrete environments. Additionally, this paper deeply presents an exploration of the use of X-ray computed tomography (X-CT) technology for analyzing the corrosion byproducts and the interface characteristics of steel bars. Recognizing the close relationship between steel bar corrosion research and microstructural properties, this paper highlights the pivotal role of X-CT in advancing this field of study. In conclusion, this paper synthesizes the current state of knowledge and provides a prospective outlook on future research directions on the corrosion of steel rebars within reinforced concrete structures. [ABSTRACT FROM AUTHOR]

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis. [ABSTRACT FROM AUTHOR]

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]

The article deals with the use of computed tomography, an advanced method for evaluating the quality of aluminium castings. Casting quality is a key factor in ensuring safety and reliability in industrial applications. Computed tomography is a comprehensive method allowing a three-dimensional, high-resolution view of the internal structure of materials. The main focus of this paper is the study of BRACKET REAR aluminium castings, manufactured in two-piece moulds using a high-pressure die-casting technology. In this paper, four castings have been analysed which are produced in one cycle. The focus is on the problem of porosity and open stagnation in the castings. A numerical simulation has also been used to illustrate the occurrence of porosity, which can be used to determine both the occurrence of porosity and the occurrence of unfilled volume. The experimental part of the paper describes the methods used to evaluate the BRACKET REAR castings. The numerical simulation was performed in ProCAST 18.0 to determine the occurrence of porosity in the castings under study. The evaluation of computed tomography was performed in myVGL 3.0 2023 software to analyse the internal defects in the castings. The evaluation focused on assessing internal defects and their subsequent effect on the functionality of the final casting. [ABSTRACT FROM AUTHOR]

Mesenteric ischemia diagnosis is challenging, with an overall mortality of up to 50% of cases despite advances in treatment. The main problem that affects the outcome is delayed diagnosis because of non-specific clinical presentation. Multi-Detector CT Angiography (MDCTA) is the first-line investigation for the suspected diagnosis of vascular abdominal pathologies and the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extent and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases, helping clinicians and surgeons with appropriate management. CT (Computed Tomography) can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM = non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA (CT Angiography) protocol; 2. to explain the CTA arterial and venous supply to the gastrointestinal tract and the relevant ischemic pattern; and 3. to describe vascular, bowel, and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia. [ABSTRACT FROM AUTHOR]

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. [ABSTRACT FROM AUTHOR]

High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector's pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse projection data, and the slices reconstructed show aliasing artifacts. Currently, aliasing artifact correction processes more medical CT images, and the number of pixels of such images is small (mainly 512 × 512 pixels). This paper presents an aliasing artifact correction algorithm based on deep learning for synchrotron radiation CT with a high pixel number ( 1728 × 1728 pixels). This method crops high-pixel-number CT images with aliasing artifacts into patches with overlapping features. During the network training process, a convolutional neural network is utilized to enhance the details of the patches, after which the patches are reintegrated into a new CT slice. Subsequently, the network parameters are updated to optimize the new CT slice that closely approximates the full-view slice. To align with practical application requirements, the neural network is trained using only three samples to optimize network parameters and applied successfully to untrained samples for aliasing artifact correction. Comparative analysis with typical deep learning aliasing artifact correction algorithms demonstrates the superior ability of our method to correct aliasing artifacts while preserving image details more effectively. Furthermore, the effect of aliasing artifact correction at varying levels of projection sparsity is investigated, revealing a positive correlation between image quality after deep learning processing and the number of projections. However, the trade-off between rapid experimentation and artifact correction remains a critical consideration. [ABSTRACT FROM AUTHOR]

A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either within the same entity or other healthcare entities. The lack of inter- and intra-entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). To develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners to improve the quality of care, the level of data security, and the efficiency of care should take stock of the state of knowledge. This paper discusses the challenges faced by medical entities in sharing and exchanging medical information seamlessly and efficiently. It highlights the need for inter- and intra-entity interoperability to improve the analysis of patient records, reduce financial costs, and enhance the quality of care. The paper reviews existing solutions proposed by various researchers and identifies their limitations. The analysis of the literature has shown that the HL7 FHIR standard is particularly well adapted for exchanging and storing health data, while DICOM, CDA, and JSON can be converted in HL7 FHIR or HL7 FHIR to these formats for interoperability purposes. This approach covers almost all use cases. [ABSTRACT FROM AUTHOR]

Simple Summary: Neurofibromatosis Type 1 is an inherited tumour predisposition syndrome with a varied clinical phenotype. Long-term monitoring through imaging is inconsistent and varies in high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. This systematic review aims to evaluate the association between a country's socioeconomic status and diagnostic modalities and strategies used for adult Neurofibromatosis Type 1 patients. Our results show multiple imaging modalities are used in high-income countries; however, there is limited use in low-income countries. The two most common diagnostic modalities used in developed countries are WB MRI and FDG PET/CT. Background: Neurofibromatosis Type 1 is an autosomal dominant tumour-predisposition condition commonly diagnosed in childhood and fully penetrant by adulthood. Long-term monitoring through imaging is inconsistent and varies between high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. We aim to systematically review international diagnostic modalities and strategies to evaluate any association between a country's socioeconomic status and diagnostic modalities or strategies used for Neurofibromatosis Type 1 patients. Methods: We searched PubMed, Embase, Web of Science, and Cochrane. Relevant clinical information on the surveillance of adult Neurofibromatosis Type 1 patients worldwide was reviewed, extracted, and synthesised. Results: We identified 51 papers reporting on 7724 individuals. Multiple imaging modalities are actively employed in high-income and upper-middle-income countries for surveying adult Neurofibromatosis Type 1 patients. We did not find any relevant papers from low- and middle-income countries. Conclusions: This systematic review suggests that there is robust data on diagnostic modalities for adult Neurofibromatosis Type 1 patients in high-income countries, but not for low- and middle-income countries. There is a lack of data on consolidated diagnostic strategies from both high- and low-income countries. Efforts should be made to publish data on usual clinical practice in low- and middle-income countries to develop clinical practice guidelines describing best medical practice to fit a local context. [ABSTRACT FROM AUTHOR]

Simple Summary: Constant technological development of modern imaging has led to substantial improvement in management and decision-making in the diagnostic and prognostic process of many different neoplasms. This also applies to cervical cancer. The main evidence, providing the base of recently updated ESGO-ESTRO-ESP recommendations (2023) on the management and treatment of cervical cancer, has been evaluated and reviewed in this paper. Ultrasound has been suggested as a valid alternative to MRI in primary diagnostic workup of cervical cancer if performed by an expert sonographer. Additionally, CT or PET/CT exhibits a substantial role in assessing the extrapelvic spread of the disease in locally advanced cases or when suspicious lymph nodes are detected. The purpose of this article is to provide a comprehensive review of the role of different imaging techniques in staging settings, displaying a focused interest in the use of ultrasound. Following the European Society of Gynaecological Oncology (ESGO), the European Society for Radiotherapy and Oncology (ESTRO), and the European Society of Pathology (ESP) joint guidelines (2018) for the management of patients with cervical cancer, treatment decisions should be guided by modern imaging techniques. After five years (2023), an update of the ESGO-ESTRO-ESP recommendations was performed, further confirming this statement. Transvaginal/transrectal ultrasound (TRS/TVS) or pelvic magnetic resonance (MRI) enables tumor delineation and precise assessment of its local extent, including the evaluation of the depth of infiltration in the bladder- or rectal wall. Additionally, both techniques have very high specificity to confirm the presence of metastatic pelvic lymph nodes but fail to exclude them due to insufficient sensitivity to detect small-volume metastases, as in any other currently available imaging modality. In early-stage disease (T1a to T2a1, except T1b3) with negative lymph nodes on TVS/TRS or MRI, surgicopathological staging should be performed. In all other situations, contrast-enhanced computed tomography (CECT) or 18F-fluorodeoxyglucose positron emission tomography combined with CT (PET-CT) is recommended to assess extrapelvic spread. This paper aims to review the evidence supporting the implementation of diagnostic imaging with a focus on ultrasound at primary diagnostic workup of cervical cancer. [ABSTRACT FROM AUTHOR]

This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models' efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage. [ABSTRACT FROM AUTHOR]

As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value. [ABSTRACT FROM AUTHOR]

Malunion of thumb and finger fractures causes problems in the cosmetic and functional aspects of the hand. Malunion of phalangeal fractures usually manifests as a combination of rotational deformities in the coronal, sagittal, and transverse planes, and corrective osteotomy is performed on the planes that cause these problems. Quantification of the deformity is essential for precise osteotomy and is difficult to perform in the transverse plane, even with radiography or computed tomography. Thus, we developed a technique called linkage simulation for the quantitative measurement of rotational deformities for surgical planning. In this procedure, finger extension and flexion can be simulated based on the predicted rotational axis of the joint, which is useful for determining the appropriate correction. Furthermore, by performing a reduction simulation in the software, it is possible to simulate the surgery and predict the postoperative results. This paper reports the details of this technique. [ABSTRACT FROM AUTHOR]

Underground gas storage (UGS) is a beneficial economic method of compensating for the imbalance between natural gas supply and demand. This paper addresses the problem of a lack of research on the two-phase distribution pattern and seepage law during the water–gas alternated flooding in gas storage reservoirs. The study constructed a three-dimensional digital core of the aquifer reservoir based on Computed Tomography (CT) scanning technology, and extracted the connecting pore structure to establish the tetrahedral mesh model. A two-phase microscopic seepage model was established based on the Volume of Fluid (VOF)method, and microscopic gas and gas–liquid two-phase unsaturated microscopic seepage simulation was carried out. The results show that the effective reservoir capacity increases with the increase in the number of alternated flooding cycles. The irreducible water is mainly distributed in the dead-end of the pore space and small pore throats, and the residual gas is mainly distributed as a band in the gas–water interface and the dead-end of the pore space of the previous round. The reservoir capacity can be increased by appropriately increasing the intensity of injection and extracting and decreasing the pressure of the reservoir. [ABSTRACT FROM AUTHOR]

Shotcrete is widely used in mine and civil engineering as supporting structure. A new type of ultra-high-strength shotcrete (UHSSC) with viscosity-enhancing agent was taken as the research object in this paper. A microstructure model of UHSSC under different curing conditions (standard curing, natural curing and film curing) was reconstructed using X-ray computed tomography (X-CT). The grey theory was used to analyze the correlation between pore characteristics and strength of UHSSC. The results showed that the porosity and the pore size of UHSSC were significantly reduced, the compressive strength was obviously improved by the new spraying process. The effects of curing conditions on the pore characteristics and compressive strength of UHSSC were obvious. Under natural curing, the hydration degree was the highest, the maximum pore size was the smallest, and the compressive strength was the highest, reaching 95.8 MPa, but the porosity was the highest. The curing condition had a certain influence on the sphericity distribution of UHSSC pores. Under film curing, the proportion of special-shaped pores (S < 0.4) was the largest and compressive strength was the smallest. There was a good correlation between pore characteristic parameters and the compressive strength of UHSSC under different curing conditions. In particular, the large pore size (D ≥ 5000 µm) and special-shaped pores (S < 0.4) had obvious effects on the strength of UHSSC, and the grey correlation coefficients were 0.8539 and 0.8080, respectively. Additionally, the pore direction of UHSSC had obvious directionality, and the anisotropy of UHSSC may be more prominent than poured specimen. The results will lay a foundation for the study of its mechanical properties and durability. [ABSTRACT FROM AUTHOR]

This paper presents a study on the influence of the layered casting technology of self-compacting concrete (SCC) on the load-bearing capacity of interlayer bond in slab elements. The research was conducted on slab elements with dimensions of 750 × 750 × 150 mm, concreted from a single point of concrete delivery. The aim of this study was to analyse the influence of the height of the concreting top layer on the bond strength between the layers. The study utilised top layer heights of 50, 75, and 100 mm, which, according to the authors' experience, are the most common cases when making slab elements. The interlayer bond was determined by investigating the splitting tensile strength of cubic specimens cut from the concrete slabs. Computed tomography (CT) was employed to image the contact zone between the concrete layers. Based on the analysis of the CT imaging and the results of the strength tests, it was shown that the interlayer bond is influenced by both the height of the top layer and its free-spread distance from the casting point. A reduction in the interlayer bond strength was observed with decreasing the height of the top layer and increasing distance from the mixture supply point. The relationships obtained were linear and had a clearly negative slope. It was concluded that the valid recommendations and standards for the multilayer casting of SCC are too general. Therefore, we propose to detail the recommendations to reduce the risk of cold joints, which diminish the bond strength of the interlayer joints [ABSTRACT FROM AUTHOR]

Material extrusion (MEX) additive manufacturing has successfully fabricated assembly-free structures composed of different materials processed in the same manufacturing cycle. Materials with different mechanical properties can be employed for the fabrication of bio-inspired structures (i.e., stiff materials connected to soft materials), which are appealing for many fields, such as bio-medical and soft robotics. In the present paper, process parameters and 3D printing strategies are presented to improve the interfacial adhesion between carbon fiber-reinforced nylon (CFPA) and thermoplastic polyurethane (TPU), which are extruded in the same manufacturing cycle using a multi-material MEX setup. To achieve our goal, a double cantilever beam (DCB) test was used to evaluate the mode I fracture toughness. The results show that the application of a heating gun (assembled near the nozzle) provides a statistically significant increase in mean fracture toughness energy from 12.3 kJ/m2 to 33.4 kJ/m2. The underlying mechanism driving this finding was further investigated by quantifying porosity at the multi-material interface using an X-ray computed tomography (CT) system, in addition to quantifying thermal history. The results show that using both bead ironing and the hot air gun during the printing process leads to a reduction of 24% in the average void volume fraction. The findings from the DCB test and X-ray CT analysis agree well with the polymer healing theory, in which an increased thermal history led to an increased fracture toughness at the multi-material interface. Moreover, this study considers the thermal history of each printed layer to correlate the measured debonding energy with results obtained using the reptation theory. [ABSTRACT FROM AUTHOR]

Micro-Computed Tomography (µCT) systems are used for examining the internal structures of various objects, such as material samples, manufactured parts, and natural objects. Resolving fine details or performing accurate geometric measurements in the voxel data critically depends on the precise calibration of the µCT systems geometry. This paper presents a calibration method for µCT systems using projections of a calibration phantom, where the coordinates of the phantom are initially unknown. The approach involves detecting and tracking steel ball bearings and adjusting the unknown system geometry parameters using non-linear least squares optimization. Multiple geometric models are tested to verify their suitability for a self-calibration approach. The implementation is tested using a calibration phantom captured at different magnifications. The results demonstrate the system's capability to determine the geometry model parameters with a remaining error on the detector between 0.27 px and 0.18 px. Systematic errors that remain after calibration, as well as changing parameters due to system instabilities, are investigated. The source code of this work is published to enable further research. [ABSTRACT FROM AUTHOR]

Congenital absence of the pericardium (CAP) is an unusual condition discovered, in most cases, incidentally but can potentially lead to fatal complications, including severe arrhythmias and sudden death. Recently, the use of modern imaging technologies has increased the diagnosis of CAP, providing important findings for risk stratification. Nevertheless, there is not yet consensus regarding therapeutic decisions, and the management of patients with CAP remains challenging. In this paper, we discuss the pathophysiological implication of CAP, review the current literature and explain the role of multimodality imaging tools for its diagnosis, management and treatment. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. [ABSTRACT FROM AUTHOR]

In recent years, material extrusion-based additive manufacturing, particularly fused filament fabrication (FFF), has gained significant attention due to its versatility and cost-effectiveness in producing complex geometries. This paper presents the characterization of seven novel materials for FFF and twenty-two commercially available filaments in terms of X-ray computed tomography (CT) numbers, as tissue mimicking materials for the realization of 3D printed radiological phantoms. Two technical approaches, by 3D printing of cube samples and by producing cylinders of melted materials, are used for achieving this goal. Results showed that the CT numbers, given in Hounsfield unit (HU), of all the samples depended on the beam kilovoltage (kV). The CT numbers ranged from +411 HU to +3071 HU (at 80 kV), from −422 HU to +3071 HU (at 100 kV), and from −442 HU to +3070 HU (at 120 kV). Several commercial and custom-made filaments demonstrated suitability for substituting soft and hard human tissues, for realization of 3D printed phantoms with FFF in CT imaging. For breast imaging, an anthropomorphic phantom with two filaments could be fabricated using ABS-C (conductive acrylonitrile butadiene styrene) as a substitute for breast adipose tissue, and ASA-A (acrylic styrene acrylonitrile) for glandular breast tissue. [ABSTRACT FROM AUTHOR]

Kidney segmentation from abdominal computed tomography (CT) images is essential for computer-aided kidney diagnosis, pathology detection, and surgical planning. This paper introduces a kidney segmentation method for clinical contrast-enhanced CT images. First, it begins with shape-based preprocessing to remove the spine and ribs. Second, a novel clustering algorithm and an initial kidney selection strategy are utilized to locate the initial slices and contours. Finally, an adaptive narrow-band approach based on active contours is developed, followed by a clustering postprocessing to address issues with concave parts. Experimental results demonstrate the high segmentation performance of the proposed method, achieving a Dice Similarity Coefficient of 97.4 ± 1.0% and an Average Symmetric Surface Distance of 0.5 ± 0.2 mm across twenty sequences. Notably, this method eliminates the need for manually setting initial contours and can handle intensity inhomogeneity and varying kidney shapes without extensive training or statistical modeling. [ABSTRACT FROM AUTHOR]

The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women's Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians' confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of element size on the microstructure of the steel–concrete interface in self-compacting concrete (SCC). Experiments were conducted on two types of test elements: a deep beam measuring 1440 × 640 × 160 mm and a wall element measuring 2240 × 1600 × 160 mm. The SCC mix was consistently pumped from the top, using a single casting point located near the formwork's edge. Horizontal steel ribbed rebars with a diameter of 16 mm were embedded in these elements. X-ray computed tomography (CT) was employed to provide three-dimensional insights into the microstructure of the rebar-to-concrete interface. An analysis of X-ray CT images from core samples revealed that the microstructure of this interface is influenced by the distance of the specimen from the mix casting point and its vertical position within the element. The combined effects of bleeding, air-pore entrapment, and plastic settlement within the SCI were observed under the top rebars. Their extent was independent of the type of element analyzed, suggesting that the deterioration of the SCI is related to the distance from the top surface of the element. These results elucidate phenomena occurring during the fresh state of concrete near reinforcing bars and their implications for bond properties. To date, some of the standards differentiate between bond conditions according to the depth of concrete beneath the rebar. In the view of the studies, this approach may be unduly rigorous. The findings offer valuable guidance for reinforced concrete execution and design. [ABSTRACT FROM AUTHOR]

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO's superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Cytomegalovirus (CMV) infection is present in a latent state in 70–90% of the immunocompetent population, and its reactivation might be triggered by inflammatory conditions such as post-COVID multisystem inflammatory syndrome (MIS-C) or by immunosuppression induced by steroids. The aim of this paper was to highlight the unexpected complications associated with SARS-CoV-2 infection that require a complex clinical approach for accurate diagnosis. Materials and Methods: We present the case of a 4-year-old male patient who, during an initially favorable course of PIMS, experienced symptoms of respiratory failure. Results: The patient initially presented with clinical and paraclinical signs of PIMS with cardiac involvement, for which high-dose corticosteroid therapy was initiated, followed by gradual tapering, along with immunoglobulins, anticoagulants, antiplatelet agents, and symptomatic treatment. After 10 days of favorable progress, the patient's general condition deteriorated, showing tachypnea, desaturation, and a ground-glass appearance on thoracic CT. Negative inflammatory markers and favorable cardiac lesion evolution ruled out MIS-C relapse. The presence of anti-CMV IgM antibodies and viral DNA in the blood confirmed acute CMV infection, likely triggered by prior severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2) infection and secondary immunosuppression due to steroids. Non-specific immunomodulatory treatment was initiated but led to worsening of pulmonary lesions, prompting the initiation of specific antiviral treatment with ganciclovir, resulting in rapid clinical and imaging improvement. Conclusions: CMV infection can be reactivated by immunosuppression induced by corticosteroid therapy for MIS-C and may require specific etiological treatment. [ABSTRACT FROM AUTHOR]

Background: Mass-forming intrahepatic cholangiocarcinoma (mICC) is the most frequent type of ICC. In contrast-enhanced computed tomography, mICC is visualized as a hypodense lesion with distal dilatation of intrahepatic bile ducts. The presented case illustrates the unusual manifestation of mICC in a 71-year-old male patient, where despite the extensive tumor mass and the hilar infiltration, the dilatation of intrahepatic bile ducts and cholestasis were not noted. Methods: A literature review on PubMed was performed. Primarily, 547 records were identified, and the titles and abstracts were systematically searched. Regarding the inclusion and exclusion criteria, 31 papers describing the non-cancerous liver lesions mimicking ICC were included in the further analysis. Results: In 41.9% of the analyzed non-cancerous lesions, the obstruction of the bile ducts was not noted, similar to our patient. A significant cholestasis has been found in 30.03% of analyzed patients. The invasion of the liver hilum was noted in one-third of the patients. Conclusions: Atypical radiological features in lesions suspected of ICC, such as the absence of intrahepatic bile-duct dilation, are common in benign lesions. In the case of radiologically atypical lesions suspected of ICC, the diagnostic imaging needs to be correlated with clinical data, and the diagnosis should be confirmed with a pathological examination. [ABSTRACT FROM AUTHOR]

From the moment the SARS-CoV-2 virus was identified in December 2019, the COVID-19 disease spread around the world, causing an increase in hospitalisations and deaths. From the beginning of the pandemic, scientists tried to determine the major cause that led to patient deaths. In this paper, the background to creating a research model was diagnostic problems related to early assessment of the degree of damage to the lungs in patients with COVID-19. The study group comprised patients hospitalised in one of the temporary COVID hospitals. Patients admitted to the hospital had confirmed infection with SARS-CoV-2. At the moment of admittance, arterial blood was taken and the relevant parameters noted. The results of physical examinations, the use of oxygen therapy and later test results were compared with the condition of the patients in later computed tomography images and descriptions. The point of reference for determining the severity of the patient's condition in the computer imagery was set for a mild condition as consisting of a percentage of total lung parenchyma surface area affected no greater than 30%, an average condition of between 30% and 70%, and a severe condition as greater than 70% of the lung parenchyma surface area affected. Patients in a mild clinical condition most frequently had mild lung damage on the CT image, similarly to patients in an average clinical condition. Patients in a serious clinical condition most often had average levels of damage on the CT image. On the basis of the collected data, it can be said that at the moment of admittance, BNP, PE and HCO3− levels, selected due to the form of lung damage, on computed tomography differed from one another in a statistically significant manner (p < 0.05). Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of a physical examination and applied oxygen therapy. Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of BNP, HCO3 and BE parameters obtained from arterial blood. [ABSTRACT FROM AUTHOR]

At present, many diseases are diagnosed by computer tomography (CT) image technology, which affects the health of the lives of millions of people. In the process of disease confrontation, it is very important for patients to detect diseases in the early stage by deep learning of 3D CT images. The paper offers a hybrid multi-instance learning model (HLFSRNN-MIL), which hybridizes high-low frequency feature fusion (HLFFF) with sequential recurrent neural network (SRNN) for CT image classification tasks. Firstly, the hybrid model uses Resnet-50 as the deep feature. The main feature of the HLFSRNN-MIL lies in its ability to make full use of the advantages of the HLFFF and SRNN methods to make up for their own weakness; i.e., the HLFFF can extract more targeted feature information to avoid the problem of excessive gradient fluctuation during training, and the SRNN is used to process the time-related sequences before classification. The experimental study of the HLFSRNN-MIL model is on two public CT datasets, namely, the Cancer Imaging Archive (TCIA) dataset on lung cancer and the China Consortium of Chest CT Image Investigation (CC-CCII) dataset on pneumonia. The experimental results show that the model exhibits better performance and accuracy. On the TCIA dataset, HLFSRNN-MIL with Residual Network (ResNet) as the feature extractor achieves an accuracy (ACC) of 0.992 and an area under curve (AUC) of 0.997. On the CC-CCII dataset, HLFSRNN-MIL achieves an ACC of 0.994 and an AUC of 0.997. Finally, compared with the existing methods, HLFSRNN-MIL has obvious advantages in all aspects. These experimental results demonstrate that HLFSRNN-MIL can effectively solve the disease problem in the field of 3D CT images. [ABSTRACT FROM AUTHOR]

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics. [ABSTRACT FROM AUTHOR]

Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods. Bulky photomultiplier tubes (PMTs) are being replaced by position-sensitive PMTs (PSPMTs), avalanche photodiodes (APDs), and silicon PMs to achieve higher detection efficiency and improved energy resolution and spatial resolution. Most recently, new SPECT cameras have been designed for cardiac imaging. The new design involves using specialised collimators in conjunction with conventional sodium iodide detectors (NaI(Tl)) or an L-shaped camera head, which utilises semiconductor detector materials such as CdZnTe (CZT: cadmium–zinc–telluride). The clinical benefits of the new design include shorter scanning times, improved image quality, enhanced patient comfort, reduced claustrophobic effects, and decreased overall size, particularly in specialised clinical centres. These noticeable improvements are also attributed to the implementation of resolution-recovery iterative reconstructions. Immense efforts have been made to establish SPECT and SPECT/CT imaging as quantitative tools by incorporating camera-specific modelling. Moreover, this review includes clinical examples in oncology, neurology, cardiology, musculoskeletal, and infection, demonstrating the impact of these advancements on clinical practice in radiology and molecular imaging departments. [ABSTRACT FROM AUTHOR]

In this paper, the limestone reservoir of the Upper Permian Taiyuan Formation in the Ordos Basin is taken as the research object. Through various analysis and testing methods, the characterization and classification evaluation of pore structure were carried out. The core porosity and pore structure characteristics were analyzed using nuclear magnetic resonance, gas measurement, and CT scanning. Based on the characteristics of the limestone reservoir, the optimal parameters of NMR testing were calibrated, the NMR testing method of limestone reservoir properties was established, and the NMR porosity of limestone was calculated. Using the core gray map obtained using CT scanning imaging technology, the three-dimensional digital core model of limestone was constructed, its pore space was extracted, and the porosity, pore fractal dimension, and tortuosity were calculated. The results show that with the thermodynamic experiment, the porosity of the sample will remain basically unchanged after 180 min, and the pressure in the sample was measured after 4 h and no air leakage was found. The T2 spectrum of saturated marlite is in the form of three peaks, two peaks, and one peak. On the whole, the p1 peak of the T2 spectrum of limestone corresponds to micropores, and most of the p2 and p3 peaks correspond to mesopores–macropores. The pore size of high porosity samples is 150–350 nm, and the micropores are well developed; the pore size of medium porosity samples is 80–150 nm, and some samples are well developed; the pore size of low porosity samples is mostly bimodal, with two peaks >> 300 nm and <<100 nm. With the increase in depth, the porosity decreases gradually, the fractal dimension decreases, and the tortuosity increases. The research results provide data support for the characterization of the pore structure of the limestone reservoir in the Taiyuan Formation of the Ordos Basin. [ABSTRACT FROM AUTHOR]

Apart from reverse-transcription polymerase chain reaction (RT-PCR) testing, chest radiographs (CXR) and computed tomography (CT) scans were employed as crucial diagnostic methods for detecting the 2019 new coronavirus disease (COVID-19). Our objective is to examine three notable COVID-19 instances from patients across the globe, along with their CXR and CT data. The evaluation of the imaging characteristics of the reported instances was the primary objective of a methodical examination of the literature. We located more than several articles that had been published between 2020 and 2023. After the papers were examined, three major cases were chosen, including a COVID-19 assessment of imaging features (chest X-ray and CT scan). Corona viral diseases (COVID-19) pose a significant risk to healthcare facilities, especially when the patient has additional medical issues. It is challenging to understand the various chest radiography results because of the use of specialized and ambiguous terminology such as "airspace disease", "pneumonia", "infiltrates", "patchy opacities", and "hazy opacities". The current investigation considered peer-reviewed case reports with Images features. Study designs, including reporting cases, were considered for imaging feature analysis. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network's feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement. [ABSTRACT FROM AUTHOR]

The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former. [ABSTRACT FROM AUTHOR]

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient's lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient's bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease. [ABSTRACT FROM AUTHOR]

Pulmonary carcinoids (PCs) are part of a spectrum of well-differentiated neuroendocrine neoplasms (NENs) and are classified as typical carcinoid (TC) and atypical carcinoid (AC). TC differ from AC not only for its histopathological features but also for its "functional imaging pattern" and prognosis. ACs are more undifferentiated and characterized by higher aggressiveness. Positron emission tomography/computed tomography (PET/CT) with somatostatin analogs (SSA) labeled with Gallium-68 (68Ga-DOTA-TOC, 68Ga-DOTA-NOC, 68Ga-DOTA-TATE) has widely replaced conventional imaging with gamma camera using 111In- or 99mTc-labelled compounds and represents now the gold standard for diagnosis and management of NENs. In this setting, as already described for gastro-entero-pancreatic NENs, 18F-Fluorodeoxiglucose ([18F]FDG) in addition to 68Ga-SSA can play an important role in clinical practice, particularly for ACs that show a more aggressive behavior compared to TCs. The aim of this systematic review is to analyze all original studies collected from the PubMed and Scopus databases regarding PCs in which both 68Ga-SSA PET/CT and [18F]FDG PET/CT were performed in order to evaluate the clinical impact of each imaging modality. The following keywords were used for the research: "18F, 68Ga and (bronchial carcinoid or carcinoid lung)". A total of 57 papers were found, of which 17 were duplicates, 8 were reviews, 10 were case reports, and 1 was an editorial. Of the remaining 21 papers, 12 were ineligible because they did not focus on PC or did not compare 68Ga-SSA and [18F]FDG. We finally retrieved and analyzed nine papers (245 patients with TCs and 110 patients with ACs), and the results highlight the importance of the combined use of 68Ga-SSA and [18F]FDG PET/CT for the correct management of these neoplasms. [ABSTRACT FROM AUTHOR]

The miniature serpentine robot can be applied to NOTES (Natural Orifice Transluminal Endoscopic Surgery). In this paper, a bronchoscopy application is addressed. This paper describes the basic mechanical design and control scheme of this miniature serpentine robotic bronchoscopy. In addition, off-line backward path planning and real-time and in situ forward navigation in this miniature serpentine robot are discussed. The proposed backward-path-planning algorithm utilizes the 3D model of a bronchial tree constructed from the synthetization of medical images such as images from CT (Computed Tomography), MRI (Magnetic Resonance Imaging), or X-ray, to define a series of nodes/events backward from the destination, for example, the lesion, to the original starting point, for example, the oral cavity. Accordingly, forward navigation is designed to make sure this series of nodes/events shall be passed/occur from the origin to the destination. This combination of backward-path planning and forward navigation does not require accurate positioning information of the tip of the miniature serpentine robot, which is where the CMOS bronchoscope is located. Collaboratively, a virtual force is introduced to maintain the tip of the miniature serpentine robot at the center of the bronchi. Results show that this method of path planning and navigation of the miniature serpentine robot for bronchoscopy applications works. [ABSTRACT FROM AUTHOR]

This paper proposes a colorization algorithm for infrared images based on a Conditional Generative Adversarial Network (CGAN) with multi-scale feature fusion and attention mechanisms, aiming to address issues such as color leakage and unclear semantics in existing infrared image coloring methods. Firstly, we improved the generator of the CGAN network by incorporating a multi-scale feature extraction module into the U-Net architecture to fuse features from different scales, thereby enhancing the network's ability to extract features and improving its semantic understanding, which improves the problems of color leakage and blurriness during colorization. Secondly, we enhanced the discriminator of the CGAN network by introducing an attention mechanism module, which includes channel attention and spatial attention modules, to better distinguish between real and generated images, thereby improving the semantic clarity of the resulting infrared images. Finally, we jointly improved the generator and discriminator of the CGAN network by incorporating both the multi-scale feature fusion module and attention mechanism module. We tested our method on a dataset containing both infrared and near-infrared images, which retains more detailed features while also preserving the advantages of existing infrared images. The experimental results show that our proposed method achieved a peak signal-to-noise ratio (PSNR) of 16.5342 dB and a structural similarity index (SSIM) of 0.6385 on an RGB-NIR (Red, Green, Blue-Near Infrared) testing dataset, representing a 5% and 13% improvement over the original CGAN network, respectively. These results demonstrate the effectiveness of our proposed algorithm in addressing the issues of color leakage and unclear semantics in the original network. The proposed method in this paper is not only applicable to infrared image colorization but can also be widely applied to the colorization of remote sensing and CT images. [ABSTRACT FROM AUTHOR]

Simple Summary: Ovarian cancer is the second most frequent gynecological cancer in Western countries and the most common cause of death due to gynecological malignancies with an estimated five-year survival rate of 39%. The high aggressiveness and mortality are mainly related to the speed of abdominal spread: 70% of patients are diagnosed at an advanced stage of disease (stage III–IV FIGO) or in the presence of peritoneal carcinomatosis (PC), and about 60% of women will develop a recurrence. In this context, imaging plays an essential role for proper staging and follow-up and in selecting patients eligible for complete cytoreduction (CCR), the most important treatment and prognostic factor for patients. PC represents the most striking picture of the loco-regional spread of ovarian cancer, configuring stage III. In the last few years, many papers have evaluated the role of imaging and therapeutic management in patients with ovarian cancer and PC. This paper summed up the literature on traditional approaches to the imaging of peritoneal carcinomatosis in advanced ovarian cancer, presenting classification systems, most frequent patterns, routes of spread and sites that are difficult to identify. The role of imaging in diagnosis was investigated, with particular attention to the reported sensitivity and specificity data—computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography-CT (PET-CT)—and to the peritoneal cancer index (PCI). In addition, we explored the therapeutic possibilities and radiomics applications that can impact management of patients with ovarian cancer. Careful staging is mandatory, and patient selection is one of the most important factors influencing complete cytoreduction (CCR) outcome: an accurate pre-operative imaging may allow selection of patients that may benefit most from primary cytoreductive surgery. [ABSTRACT FROM AUTHOR]

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: The gap in understanding of radiation therapy (RT) procedures among non-radiation oncologists poses a significant barrier to optimal cancer care. Aimed at equipping non-oncologists, medical students, and non-clinical researchers with crucial insights into RT, this paper serves as a comprehensive guide that demystifies the intricate, multi-step journey from treatment planning to patient follow-up. Highlighting the indispensable role of radiation oncologists (ROs) in this process, the paper delves into pivotal phases such as CT simulation, treatment target and organs at risk (OAR) delineation, and acute toxicity management. Ultimately, the manuscript aims to significantly elevate patient care standards by bridging the knowledge gap in non-oncology healthcare circles about RT. Its significance lies in its potential to foster improved interdisciplinary collaboration, thereby enhancing the effectiveness and safety of cancer treatment regimens. Background: Multidisciplinary management is crucial in cancer diagnosis and treatment. Multidisciplinary teams include specialists in surgery, medical therapies, and radiation therapy (RT), each playing unique roles in oncology care. One significant aspect is RT, guided by radiation oncologists (ROs). This paper serves as a detailed primer for non-oncologists, medical students, or non-clinical investigators, educating them on contemporary RT practices. Methods: This report follows the process of RT planning and execution. Starting from the decision-making in multidisciplinary teams to the completion of RT and subsequent patient follow-up, it aims to offer non-oncologists an understanding of the RO's work in a comprehensive manner. Results: The first step in RT is a planning session that includes obtaining a CT scan of the area to be treated, known as the CT simulation. The patients are imaged in the exact position in which they will receive treatment. The second step, which is the primary source of uncertainty, involves the delineation of treatment targets and organs at risk (OAR). The objective is to ensure precise irradiation of the target volume while sparing the OARs as much as possible. Various radiation modalities, such as external beam therapy with electrons, photons, or particles (including protons and carbon ions), as well as brachytherapy, are utilized. Within these modalities, several techniques, such as three-dimensional conformal RT, intensity-modulated RT, volumetric modulated arc therapy, scattering beam proton therapy, and intensity-modulated proton therapy, are employed to achieve optimal treatment outcomes. The RT plan development is an iterative process involving medical physicists, dosimetrists, and ROs. The complexity and time required vary, ranging from an hour to a week. Once approved, RT begins, with image-guided RT being standard practice for patient alignment. The RO manages acute toxicities during treatment and prepares a summary upon completion. There is a considerable variance in practices, with some ROs offering lifelong follow-up and managing potential late effects of treatment. Conclusions: Comprehension of RT clinical effects by non-oncologists providers significantly elevates long-term patient care quality. Hence, educating non-oncologists enhances care for RT patients, underlining this report's importance. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most dangerous cancers in the world, and its early clinical manifestation is malignant nodules in the lungs, so nodule detection in the lungs can provide the basis for the prevention and treatment of lung cancer. In recent years, the development of neural networks has provided a new paradigm for creating computer-aided systems for pulmonary nodule detection. Currently, the mainstream pulmonary nodule detection models are based on convolutional neural networks (CNN); however, as the output of a CNN is based on a fixed-size convolutional kernel, it can lead to a model that cannot establish an effective long-range dependence and can only model local features of CT images. The self-attention block in the traditional transformer structures, although able to establish long-range dependence, are as ineffective as CNN structures in dealing with irregular lesions of nodules. To overcome these problems, this paper combines the self-attention block with the learnable regional attention block to form the multifaceted attention block, which enables the model to establish a more effective long-term dependence based on the characteristics of pulmonary nodules. And the multifaceted attention block is intermingled with the encoder–decoder structure in the CNN to propose the 3D multifaceted attention encoder–decoder network (MAED), which is able to model CT images locally while establishing effective long-term dependencies. In addition, we design a multiscale module to extract the features of pulmonary nodules at different scales and use a focal loss function to reduce the false alarm rate. We evaluated the proposed model on the large-scale public dataset LUNA16, with an average sensitivity of 89.1% across the seven predefined FPs/scan criteria. The experimental results show that the MAED model is able to simultaneously achieve efficient detection of pulmonary nodules and filtering of false positive nodules. [ABSTRACT FROM AUTHOR]