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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]

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]

With the continuous progress of technology, computed tomography (CT) technology has expanded from medicine to agriculture and other industries. With the advantages of non-destructiveness, high resolution, and high precision, CT technology shows great application potential in the agricultural field. However, there are still some problems with this technology that need to be solved. This paper aims to show the application of CT technology in the agricultural field, find technical challenges, and put forward specific countermeasures, so that CT technology can be better applied in the agricultural field. This paper summarizes the application of CT technology in the quality detection of agricultural products, disease and insect pest identification, seed screening, soil analysis, and precision agriculture management, and focuses on the current challenges and the countermeasures, and looks into the role of this technology in promoting agricultural development in the future. Despite various challenges, CT technology has far more advantages than disadvantages, and it is expected to become an indispensable part of all the links of agricultural production and promote the development of precision agriculture and smart agriculture. [ABSTRACT FROM AUTHOR]

To investigate the distribution characteristics of the micropore structure in high-performance self-compacting concrete (C80), high-resolution X-ray computed tomography, AVIZO software (version 2024.1), and scanning electron microscopy were employed to observe and analyze the internal pore structure of C80 self-compacting concrete specimens. The main conclusions are as follows: There is a large number of pore structures within the carbonate rock-based high-performance self-compacting concrete. At a testing precision range of 10 μm, the micropores exhibit a circular feature with good overall circularity. Observations through SEM, scanning electron microscopy, reveal that there are micro-cracks or interconnected crack structures within the high-performance concrete, with widths ranging from 0.5 to 2 μm, and the sample contains tiny voids of 3 to 10 μm. A statistical analysis of the micropores within the carbonate rock-based self-compacting concrete indicates that the pore diameter follows a three-parameter normal distribution. Due to the limitations of experimental observation and precision, the experimental statistical results show a positively skewed (high peak and left-skewed) phenomenon. This paper proposes a "correction of skewed peak" method for the analysis and discussion of the calculation of the "third parameter C" in the statistical results. The results show that the method proposed in this paper can quickly, objectively, and optimally determine the third parameter, compensating for the missing data not accounted for below 10 μm and the limitations of the finite number of experimental samples, providing a reference for examining the distribution of pores within concrete. [ABSTRACT FROM AUTHOR]

As yet, no systematic review on commercial deep learning-based auto-segmentation (DLAS) software for breast cancer radiation therapy (RT) planning has been published, although NRG Oncology has highlighted the necessity for such. The purpose of this systematic review is to investigate the performances of commercial DLAS software packages for breast cancer RT planning and methods for their performance evaluation. A literature search was conducted with the use of electronic databases. Fifteen papers met the selection criteria and were included. The included studies evaluated eight software packages (Limbus Contour, Manteia AccuLearning, Mirada DLCExpert, MVision.ai Contour+, Radformation AutoContour, RaySearch RayStation, Siemens syngo.via RT Image Suite/AI-Rad Companion Organs RT, and Therapanacea Annotate). Their findings show that the DLAS software could contour ten organs at risk (body, contralateral breast, esophagus-overlapping area, heart, ipsilateral humeral head, left and right lungs, liver, and sternum and trachea) and three clinical target volumes (CTVp_breast, CTVp_chestwall, and CTVn_L1) up to the clinically acceptable standard. This can contribute to 45.4%–93.7% contouring time reduction per patient. Although NRO Oncology has suggested that every clinical center should conduct its own DLAS software evaluation before clinical implementation, such testing appears particularly crucial for Manteia AccuLearning, Mirada DLCExpert, and MVision.ai Contour+ as a result of the methodological weaknesses of the corresponding studies such as the use of small datasets collected retrospectively from single centers for the evaluation. [ABSTRACT FROM AUTHOR]

Background: Spontaneous intracranial hypotension (SIH) is a rare and frequently misdiagnosed disorder characterized by a low volume of cerebrospinal fluid (CSF) caused by the leakage of CSF through the spinal dural membrane. Patients with Marfan Syndrome (MS) and other connective tissue disorders are at an increased risk for dural ectasia, which may predispose them to spontaneous CSF leaks due to the structural weakness of their dural membranes. The management of SIH in MS patients is debated. Conservative measures, an epidural blood patch (EBP), and surgical treatments are the options generally provided. Methods: Herein, we report on the case of a 52-year-old female affected by MS, genetically confirmed, with a two-month history of sudden-onset, "thunderclap" headache, worsened in an upright position and horizontal diplopia. A Computed Tomography (CT) scan of the brain showed a bilateral chronic subdural hematoma, slit ventricles, and a caudal descent of the brainstem without overt tonsillar herniation. The Magnetic Resonance Imaging (MRI) scan of the whole spine revealed dural ectasia in the lumbosacral area and presacral perineural cyst without extradural CSF collection. The case was successfully managed with bed rest and high-dose corticosteroid therapy. Then, we discuss the pertinent literature, consisting of 25 papers dealing with the treatment of SIH in patients affected by MS. Results: The literature review yielded 25 papers dealing with SIH management in patients with MS, including 28 patients overall; 21 patients underwent EBP, of whom 7 patients had multiple procedures. Overall, in 23 cases (82%), the symptoms improved. In three cases, the patients were managed conservatively with bed rest. In three of these cases, there was an improvement. In one case, the surgical fenestration of two lumbar intradural spinal meningeal cysts was performed and the patient improved after the procedure. Our patient underwent 15 days of steroid therapy (dexamethasone iv 12 mg/day for 7 days, then reduced to 4 mg/day) and intravenous hydration (Ringer lactate 1500 mL/day). In ten days, the symptoms disappeared. At the 6-month follow-up, the patient was in good clinical condition, and a CT scan showed an almost complete regression of the bilateral subdural hematoma. Conclusions: The management of SIH in MS patients is still challenging. Patients with connective tissue disorders such as MS are at an increased risk for SIH. Few studies have assessed the management of these patients and different strategies. Our case and the available literature provide further data for this type of case. [ABSTRACT FROM AUTHOR]

Recent research has proposed using positron emission tomography/computed tomography (PET/CT) along with the administration of prostate-specific membrane antigen (PSMA)-targeting radiopharmaceuticals to identify breast cancer (BC) lesions. An extensive literature review to investigate the possible diagnostic utility of PET/CT with PSMA-targeting radiopharmaceuticals in BC patients was performed. The research comprised different clinical scenarios, including both newly diagnosed BC patients and those who had experienced disease relapse. This updated systematic review encompassed six studies investigating the diagnostic efficacy of PSMA-targeted PET/CT in BC. Throughout all clinical settings investigated, the papers presented data demonstrating a modest diagnostic performance of PSMA-targeted PET/CT in different subtypes of BC. In this setting, PSMA-guided PET/CT showed slightly higher accuracy in patients diagnosed with triple-negative BC. Based on the current literature, PSMA-targeted PET/CT cannot be suggested as a diagnostic tool to assess BC extent in any clinical scenario. However, based on the PSMA expression observed in triple-negative patients, it can be proposed as a tool to evaluate whether BC patients could benefit from PSMA-targeting radioligand therapy. [ABSTRACT FROM AUTHOR]

In CT-guided percutaneous punctures—an image-guided puncture method using CT images—physicians treat targets such as lung tumors, liver tumors, renal tumors, and intervertebral abscesses by inserting a puncture needle into the body from the exterior while viewing images. By recognizing two-dimensional CT images prior to a procedure, a physician determines the least invasive puncture route for the patient. Therefore, the candidate puncture route is limited to a two-dimensional region along the cross section of the human body. In this paper, we aim to construct a three-dimensional puncture space based on multiple two-dimensional CT images to search for a safer and shorter puncture route for a given patient. If all puncture routes starting from a target in the three-dimensional space were examined from all directions (the brute-force method), the processing time to derive the puncture route would be very long. We propose a more efficient method for three-dimensional puncture route selection in CT-guided percutaneous punctures. The proposed method extends the ray-tracing method, which quickly derives a line segment from a given start point to an end point on a two-dimensional plane, and applies it to three-dimensional space. During actual puncture route selection, a physician can use CT images to derive a three-dimensional puncture route that is safe for the patient and minimizes the puncture time. The main novelty is that we propose a method for deriving a three-dimensional puncture route within the allowed time in an actual puncture. The main goal is for physicians to select the puncture route they will use in the actual surgery from among the multiple three-dimensional puncture route candidates derived using the proposed method. The proposed method derives a three-dimensional puncture route within the allowed time in an actual puncture. Physicians can use the proposed method to derive a new puncture route, reducing the burden on patients and improving physician skills. In the evaluation results of a computer simulation, for a 3D CT image created by combining 170 two-dimensional CT images, the processing time for deriving the puncture route using the proposed method was approximately 59.4 s. The shortest length of the puncture route from the starting point to the target was between 20 mm and 22 mm. The search time for a three-dimensional human body consisting of 15 CT images was 4.77 s for the proposed method and 2599.0 s for a brute-force method. In a questionnaire, physicians who actually perform puncture treatments evaluated the candidate puncture routes derived by the proposed method. We confirmed that physicians could actually use these candidates as a puncture route. [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]

The CT (computed tomography) scanner has been used for many years now not only for medical measurements but also in many industries, for example, in defectoscopy for measuring sheet thickness and checking the joining of materials, as well as for measuring the geometry of individual components. This type of scanner is a good complement to coordinate contact and non-contact measurements for intra-structural measurements and inaccessible places. The variety of materials, however, makes it very difficult to select individual CT parameters. In this paper, a curve for selecting the maximum and minimum voltage of the lamp depending on the density of a given material is determined and an interpolation polynomial (1d with a third-degree polynomial) is used, by defining third-degree glued functions (cubic spline) to determine intermediate voltage values to a given material density, so as to determine full data ranges. This approach can facilitate the work of selecting scanning parameters for non-destructive testing, as this is a difficult process and sometimes consumes half of the measurement time. The practical experiments were carried out at the Accredited Coordinate Metrology Laboratory to develop a multi-criteria matrix for selecting CT measurement parameters for measurement accuracy. This approach reduced the time by an average of half an hour and effectively optimized the selection of scanning parameters. [ABSTRACT FROM AUTHOR]

The permeability characteristics of tailings directly affect the position of the infiltration line of the tailings dam, which is the most critical factor affecting tailings dam failures. In order to fully analyze the essence of its permeability characteristics, computed tomography (CT) technology is used to analyze the structure of different types of tailings from a microscopic perspective and carry out microscopic seepage simulation. The results showed the following findings: (1) The porosity of viscous tailings ranges from 25 to 35%, the distribution of surface porosity along the height is relatively uniform, and the distribution is shown as having a certain discrete nature with the increase in particle size. (2) Compared with silty and sandy tailings, the surface of viscous tailings is smoother and more round, and the shape factor can reach 0.95; (3) The data gap between the simulation and the measurements by CT scanning technology is less than 10%, and the estimation of the permeability characteristics is feasible, with good applicability in the simulation of tailings seepage. (4) In the microscopic pore throat structure, the permeability characteristics of the tailings are more affected by the radius of the throat than the pore radius, and the exponential function relationship between the permeability coefficient and the porosity satisfies a high correlation. In this paper, the relationship between the microstructure and permeability characteristics of tailings is analyzed by CT technology; the permeability is simulated and calculated, and a permeability coefficient prediction model for tailings is proposed in combination with the experiment, which can provide a new idea and method for the study of the permeability characteristics of tailings. [ABSTRACT FROM AUTHOR]

The egg has natural barriers that prevent microbiological contamination and promote food safety. The use of non-destructive methods to obtain morphometric measurements of chicken eggs has the potential to replace traditional invasive techniques, offering greater efficiency and accuracy. This paper aims to demonstrate that estimates derived from non-invasive approaches, such as 3D computed tomography (CT) image analysis, can be comparable to conventional destructive methods. To achieve this goal, two widely recognized deep learning architectures, U-Net 3D and Fully Convolutional Networks (FCN) 3D, were modeled to segment and analyze 3D CT images of chicken eggs. A dataset of real CT images was created and labeled, allowing the extraction of important morphometric measurements, including height, width, shell thickness, and volume. The models achieved an accuracy of up to 98.69%, demonstrating their effectiveness compared to results from manual measurements. These findings highlight the potential of CT image analysis, combined with deep learning, as a non-invasive alternative in industrial and research settings. This approach not only minimizes the need for invasive procedures but also offers a scalable and reliable method for egg quality assessment. [ABSTRACT FROM AUTHOR]

Background: Tibiofibula fractures occur across all age groups, and postoperative complications are frequent. An accurate and rapid classification methodology for these fractures could significantly assist physicians. Clinically, tibiofibula fractures occur at various locations, and the fracture types are not evenly distributed. Methods: This paper presents a deep learning model for the interpretable multi-label classification of tibiofibula fractures in two-dimensional (2D) CT scan images, addressing the challenges posed by a limited sample size and an uneven distribution of fracture types. We retrospectively collected 2494 2D CT images from 168 patients with tibia or fibula fractures. The types of fractures identified in the CT scan images were classified according to the AO/OTA fracture classification. A deep learning model was developed to classify composite fractures in 2D CT images, providing visual interpretation for each identified class. The visual interpretation was given with the saliency maps constructed by the Grad-CAM++ method. The deep learning model was trained using data augmentation techniques to address class imbalance and the limited dataset size. Results: Our experiments demonstrated that the proposed model achieved a mean average precision (mAP) of 95.71%. Conclusions: The saliency map-based visual interpretation enables the verification of whether the model provides reliable decision-making for classification. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients' quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications. [ABSTRACT FROM AUTHOR]

3D printing has become essential to many fields for its low-cost production and rapid prototyping abilities. As 3D printing becomes an alternative manufacturing tool, developing methods to non-destructively evaluate defects for quality control is essential. This study integrates the non-destructive terahertz (THz) analysis methods of terahertz time-domain spectroscopy (THz-TDS) and terahertz computed tomography (THz CT) to image and assess 3D printed resin structures for defects. The terahertz images were reconstructed using MATLAB, and the rotational symmetry of various structures before and after the introduction of defects was evaluated by calculating the mean squared deviation (MSD), which served as a symmetry parameter to indicate the presence of defects. Structures A and B had MSD values that were at least three standard deviations larger after introducing defects to their structures, showing a significant change in symmetry and indicating the existence of defects. Similarly, in structure C, blockages in parts made with different post-cures were identified based on the increase in MSD values for those slices. For structure D, the presence of a defect increased the MSD value by 14%. The results of this study verify that the MSD calculated for the rotational symmetry of the structures was greater when defects were present, accurately reflecting the anticipated breaks in symmetry. This paper demonstrates that terahertz imaging, combined with MSD analysis, is a viable procedure to identify and quantify defects in rotationally symmetric 3D printed structures. [ABSTRACT FROM AUTHOR]

Background: Lung cancer still maintains the leading position among causes of death in the world; the process of early detection surely contributes to changes in the survival of patients. Standard diagnostic methods are grossly insensitive, especially in the early stages. In this paper, radiomic features are discussed that can assure improved diagnostic accuracy through automated lung cancer detection by considering the important feature categories, such as texture, shape, and intensity, originating from the CT DICOM images. Methods: We developed and compared the performance of two machine learning models—DenseNet-201 CNN and XGBoost—trained on radiomic features with the ability to identify malignant tumors from benign ones. Feature importance was analyzed using SHAP and techniques of permutation importance that enhance both the global and case-specific interpretability of the models. Results: A few features that reflect tumor heterogeneity and morphology include GLCM Entropy, shape compactness, and surface-area-to-volume ratio. These performed excellently in diagnosis, with DenseNet-201 producing an accuracy of 92.4% and XGBoost at 89.7%. The analysis of feature interpretability ascertains its potential in early detection and boosting diagnostic confidence. Conclusions: The current work identifies the most important radiomic features and quantifies their diagnostic significance through a properly conducted feature selection process reflecting stability analysis. This provides the blueprint for feature-driven model interpretability in clinical applications. Radiomics features have great value in the automated diagnosis of lung cancer, especially when combined with machine learning models. This might improve early detection and open personalized diagnostic strategies for precision oncology. [ABSTRACT FROM AUTHOR]

Background: Brain calcifications, found in various conditions, may be incidental or crucial for diagnosis. They occur in physiological changes, infections, genetic diseases, neurodegenerative conditions, vascular syndromes, metabolic disorders, endocrine disorders, and primary tumors like oligodendroglioma. While often incidental, their presence can be vital for accurate diagnosis. Brain metastases are the most common neoplastic lesions in adults, with their incidence increasing due to improved diagnostic tools and overall oncologic patient survival. Calcifications within brain metastases are uncommon, mostly seen in patients treated with radiation therapy (RT). Although cases of calcified brain metastasis (CBM) are reported, large recent studies are scarce and the real incidence remains unclear. This lack of data raises the risk of underestimating CBM in the differential diagnosis of brain calcifications, potentially leading to misdiagnosis and delayed treatment, particularly when calcifications are observed without prior RT. Aim: This systematic review sought to assess the incidence of CBM in patients with identified primary tumors who underwent brain chemotherapy (CT) for staging. Additionally, the study aimed to explore the primary tumor types more frequently linked to CBM and determine whether CBM manifested initially or post-RT. Methods: A comprehensive search was performed across prominent medical databases (PubMed, Cochrane Library, and Embase) until 20 January 2024. The employed search method incorporated pertinent Medical Subject Headings (MeSH) and keywords such as "calcification", "brain metastasis", and "CT scan". Studies included in this review were publications focusing on CBM in patients with identified primary tumors who underwent brain CT for staging. Results: In a systematic review of 39 studies on CBM in patients with identified primary tumors, 98 papers were initially identified, with 52 chosen for full-text analysis. Among them, 39 were deemed eligible after excluding 13 for various reasons. The study investigates brain calcifications in 1115 patients with metastatic disease, revealing that 7.89% had brain metastases, with 25% showing calcifications ab initio. These calcifications were more common than previously reported, emphasizing the need for attention to intraparenchymal brain calcifications in oncologic patients. Most CBM originated from lung and breast adenocarcinomas, and their correlation with primary tumor calcifications was inconclusive. Conclusions: The study highlights the significance of identifying evolving lesions in oncologic patients, calling for increased awareness among neuroradiologists and shedding light on the prevalence and characteristics of CBM. [ABSTRACT FROM AUTHOR]

In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient's own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. The model uses wavelet transform to extract different components of the image and performs fusion and enhancement processing as the input to the model. The model performs multiple registrations from local to global regions. Then, we propose a novel shared pyramid registration network that can accurately extract features from different modalities, optimizing the predicted deformation field through progressive refinement. In order to improve the registration performance, we also propose a deep learning similarity measurement method combined with bistructural morphology. On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. The model was verified by the actual clinical data of cervical cancer patients. After a large number of experiments, our proposed model achieved the highest dice similarity coefficient (DSC) metric compared with the state-of-the-art registration methods. The DSC index of the MTEF algorithm is 5.64% higher than that of the TransMorph algorithm. It will effectively integrate multi-modal image information, improve the accuracy of tumor localization, and benefit more cervical cancer patients. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, often only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model. [ABSTRACT FROM AUTHOR]

Artifacts in computed tomography and X-ray microtomography are image distortions caused by various factors. Some can be reduced before or during the examination, while others are removed algorithmically after image acquisition. The latter group includes metallic artifacts caused by metal objects in the sample. This paper proposes a new method for eliminating metallic artifacts, applying a bone- and soft-tissue decomposition (BSTD) algorithm to microtomography raw data before the reconstruction process. We show that the decomposition algorithm can effectively remove metallic artifacts in microCT images, which increases the image contrast and allows for better visualization of regions near the metallic elements. For quantity analysis, we computed SSIM and PSNR factors, and we observed values increasing from 0.97 to 0.99 and from 40 dB to 43 dB, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiation therapy is becoming increasingly important in the treatment of liver and pancreatic tumors, particularly in situations where high doses of radiation can be delivered safely. However, there are several challenges to treating tumors in the abdomen, including poor visibility of the tumor and movements due to breathing and digestion. The typical imaging available at the time of treatment makes it difficult to see both the tumor and nearby portions of the digestive tract, which is particularly sensitive to radiation damage. This paper describes the workflow involved when using high-quality computed tomography imaging at the time of treatment, to ensure that the tumor is accurately targeted and normal tissues are avoided. This study shows that by using these images and the planned dose distribution, the dose to normal structures can be maintained below specified targets. With this technology and workflow, more patients can benefit from high-dose radiation treatment to the liver and pancreas. Background/Objectives: Stereotactic body radiation therapy (SBRT) for abdominal targets faces a variety of challenges, including motion caused by the respiration and digestion and a relatively poor level of contrast between the tumor and the surrounding tissues. Breath-hold treatments with computed tomography-on-rails (CTOR) image guidance is one way of addressing these challenges, allowing for both the tumor and normal tissues to be well-visualized. Using isodose lines (IDLs) from CT simulations as a guide, the anatomical information can be used to shift the alignment or trigger a replan, such that normal tissues receive acceptable doses of radiation. Methods: This study aims to describe the workflow involved when using CTOR for pancreas and liver SBRT and demonstrates its effectiveness through several case studies. Results: In these case studies, using the anatomical information gained through diagnostic-quality CT guidance to make slight adjustments to the alignment, resulted in reductions in the maximum dose to the stomach. Conclusions: High-quality imaging, such as CTOR, and the use of IDLs to estimate the doses to OARs, enable the safe delivery of SBRT, without the added complexity and resource commitment required by daily online adaptive planning. [ABSTRACT FROM AUTHOR]

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility. [ABSTRACT FROM AUTHOR]

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field. [ABSTRACT FROM AUTHOR]

Compared with traditional invasive coronary angiography (ICA), coronary CT angiography (CCTA) has the advantages of being rapid, economical, and minimally invasive. The wide-detector CT, with its superior temporal resolution and robust three-dimensional reconstruction technology, thus enables CCTA in patients with high heart rates and arrhythmias, leading to a high potential for clinical application. This paper systematically summarizes wide-detector CT hardware configurations of various vendors routinely used for CCTA examinations and reviews the effects of patient heart rate and heart rate variability, scanning modality, reconstruction algorithms, tube voltage, and scanning field of view on image quality and radiation dose. In addition, novel technologies in the field of CT applied to CCTA examinations are also presented. Since this examination has a diagnostic accuracy that is highly consistent with ICA, it can be further used as a routine examination tool for coronary artery disease in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required. [ABSTRACT FROM AUTHOR]

As a novel pavement wear layer material, the micromechanical mechanisms of High-toughness Ultra-thin Friction Course (HUFC) have not been fully elucidated. This paper presents a new method for the three-dimensional micromechanical simulation of high-toughness asphalt mixtures based on a viscoelastic parameter calibration model. X-ray Computerized Tomography (CT) was employed to scan samples of high-toughness asphalt mixtures to obtain detailed information on the internal structure (aggregate, fine aggregate matrix FAM and voids), and a three-dimensional micromechanical model was constructed based on the real-scale distribution of these components. Aggregates in the high-toughness asphalt mixture were modeled as elastic bodies, while FAM was treated as a viscoelastic material characterized by the Burgers model. Using the Boltzmann linear superposition principle and Laplace transform theory, the viscoelastic properties of FAM were converted into Prony parameters recognizable by finite element software, and the viscoelastic parameters were calibrated. Micromechanical simulations were conducted for three different gradings of high-toughness asphalt mixtures, and the results show that the predicted deformation closely matched the measured deformation. This method accurately reflects the deformation characteristics of different gradings of high-toughness asphalt mixtures, overcoming the limitations of traditional numerical simulations based on homogeneous material models. It represents an advancement and refinement of micromechanical simulation methods for high-toughness asphalt mixtures. [ABSTRACT FROM AUTHOR]

Simple Summary: The spine is the most common site of osseous metastatic involvement. Advances in diagnosing and treating spinal metastasis have improved patient survival and quality of life. There is now a strong focus on customized imaging to enhance treatment planning for tumor ablation and reduce treatment-related complications. This review discusses imaging protocols and findings in the pre- and post-SBRT setting and the imaging features differentiating expected post-SBRT treatment changes from LF. Spine metastases (SMs) are common, arising in 70% of the cases of the most prevalent malignancies in males (prostate cancer) and females (breast cancer). Stereotactic body radiotherapy, or SBRT, has been incorporated into clinical treatment algorithms over the past decade. SBRT has shown promising rates of local control for oligometastatic spinal lesions with low radiation dose to adjacent critical tissues, particularly the spinal cord. Imaging is critically important in SBRT planning, guidance, and response monitoring. This paper reviews the roles of imaging in spine SBRT, including conventional and advanced imaging approaches for SM detection, treatment planning, and post-SBRT follow-up. [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]

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]

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]

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]

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]

Simple Summary: This review paper explores advanced imaging technologies for prostate cancer because traditional methods often miss or misclassify cancers. The aim of this study is to provide a comprehensive overview of three key technologies, MRI, micro-ultrasound, and prostate-specific membrane antigen (PSMA) PET scans, and how they can improve prostate cancer detection and management. The findings showed that these techniques have improved our ability to detect prostate cancer; however, further studies are needed to determine each imaging technique's specific role in the diagnosis of prostate cancer. Background/Objectives: Prostate cancer is a prevalent malignancy often presenting without early symptoms. Advanced imaging technologies have revolutionized its diagnosis and management. This review discusses the principles, benefits, and clinical applications of multiparametric magnetic resonance imaging (mpMRI), micro-ultrasound (microUS), and prostate-specific membrane antigen positron emission tomography–computed tomography (PSMA PET/CT) in localized prostate cancer. Methods: We conducted a comprehensive literature review of recent studies and guidelines on mpMRI, microUS, and PSMA PET/CT in prostate cancer diagnosis, focusing on their applications in biopsy-naïve patients, those with previous negative biopsies, and patients under active surveillance. Results: MpMRI has demonstrated high sensitivity and negative predictive value in detecting clinically significant prostate cancer (csPCa). MicroUS, a newer technology, has shown promising results in early studies, with sensitivity and specificity comparable to mpMRI. PSMA PET/CT has emerged as a highly sensitive and specific imaging modality, particularly valuable for staging and detecting metastatic disease. All three technologies have been incorporated into urologic practice for prostate cancer diagnosis and management, with each offering unique advantages in different clinical scenarios. Conclusions: Advanced imaging techniques, including mpMRI, microUS, and PSMA PET/CT, have significantly improved the accuracy of prostate cancer diagnosis, staging, and management. These technologies enable more precise targeting of suspicious lesions during biopsy and therapy planning. However, further research, especially randomized controlled trials, is needed to fully establish the optimal use and inclusion of these imaging modalities in various stages of prostate cancer care. [ABSTRACT FROM AUTHOR]

Objectives: Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has gained a primary role in prostate cancer (PCa) imaging, overcoming conventional imaging and prostate-specific antigen (PSA) serum levels, and has recently emerged as a promising technique for monitoring therapy response in metastatic castration-resistant prostate cancer (mCRPC) patients treated with novel hormonal therapy, taxanes, and radioligand therapy (RLT). In this review, we aim to provide an overview of the most relevant aspects under study and future prospects related to the prognostic role of PSMA PET/CT in mCRPC. Methods: A systematic literature search was performed in the following databases: MEDLINE, PubMed, and EMBASE databases. The study focused exclusively on English-language studies, excluding papers not pertinent to the topic. Results: PSMA PET imaging offers a higher sensitivity and specificity than conventional imaging and provides accurate staging and efficient diagnosis of distant metastases. The data presented herein highlight the usefulness of PET in risk stratification, with a prognostic potential that can have a significant impact on clinical practice. Several prospective trials are ongoing and will shortly provide more evidence supporting the prognostic potential of PET PSMA data in this clinical scenario. Conclusions: Current evidence proves the prognostic role of PSMA PET/CT in different settings, with raising relevance also in the context of mCRPC. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this paper is to describe our experience in the embolization of hypervascular giant thoracic tumors before surgical excision. Methods: A single-center retrospective review of five trans-arterial preoperative embolization procedures executed between October 2020 and July 2024. Patients' demographics, anatomical aspects, feasibility, technique, and outcomes were reviewed. Results: In all cases, accurate targeting and safe embolization was achieved, with satisfactory devascularization evaluated with post-procedural angiography and with minimal blood loss during subsequent surgical operation. Conclusions: In our experience, preoperative embolization of giant thoracic masses has been technically feasible, safe, and effective in reducing tumor vascularization, thus facilitating surgical treatment. This approach should be evaluated as an option, especially in patients with hypervascular thoracic tumors. [ABSTRACT FROM AUTHOR]

This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing. [ABSTRACT FROM AUTHOR]

Breakthrough pressure of natural gas in rock is an important evaluation parameter in gas reservoir development. In this study, experimental measurements of porosity and permeability of the caprock core were carried out. A digital core model was obtained by body rendering of rock CT (Computed Tomography) slices using Avizo software. Then, the grid model was imported into Fluent software for a numerical simulation of natural gas breakthrough pressure in the core. The innovations of this paper include (1) an accurate calculation method of cap breakthrough pressure based on the VOF method; (2) the method performs a perceptual analysis of average pore radius, wettability, roughness height, and viscosity ratio. [ABSTRACT FROM AUTHOR]