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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]

The article announces the 10th International Symposium on Digital Industrial Radiology and Computed Tomography (DIR 2025), hosted by COFREND, CEA List, and DGZfP, focusing on promoting knowledge exchange and advancements in digital industrial radiology and computed tomography. Scientists, users, equipment suppliers, and academic and industrial participants interested in non-destructive testing (NDT)/non-destructive evaluation (NDE) applications are encouraged to attend.

This paper presents the use of X-ray computed tomography and X-ray fluorescence in the analysis and expert research of the seventeenth century printed book "Eukhologīon albo Molitoslov, ili Trebnik" from Kiev. The main purpose of the survey was to confirm whether the book binding is original or whether it is a rebinding, and whether there are any fragments of the hidden older texts. Commonly used radiography is usually not able to provide sufficient information for these purposes. On the other hand, computed tomography allows a detailed and three-dimensional documentation of the bookbinding technology and the structure of the materials used, including the wooden boards. It will be presented that all elements of the weave are clearly visible, making it possible to show that there are no internal defects in the stitching and materials. It has also been convincingly shown that there are no fragments or layers of older texts in the binding, so no further invasive intervention will be necessary regarding this aspect. The paper also demonstrates the possibility of reading the text in a closed book utilising X-ray computed tomography data; this option may be advantageous for massively damaged manuscripts. It will also be shown, that thanks to detailed tomographic imaging of the wood structure of the boards, a dendrochronological survey can be successfully carried out without invasive intervention into their outer layers. From the CT data it was also found that the pigments of the letters have significantly different densities. Therefore, as part of the survey, elemental analysis of the writing was also carried out using a portable X-ray fluorescence spectrometer to confirm and clarify this finding. [ABSTRACT FROM AUTHOR]

An attempt is made in this paper to diagnose brain-related diseases like sarcoma, fatal stroke disease, cerebral disease, and Alzheimer's disease by using saliency information from magnetic resonance and computed tomography source images. The saliency information for each source image is computed using guided and median filters. The obtained saliency maps are used for computing the weight maps of each source image by using image statistics. The obtained weights are used to fuse the approximate and detailed layers of the source images by using the weighted average fusion technique. The proposed algorithm is simulated in MATLAB for various benchmark data sets of brains taken from the brain atlas provided by Harvard Medical School, available at https://www.med.harvard.edu/aanlib. In order to test the efficacy of the novel method, comparative analysis is performed in terms of image quality assessment metrics like mean, mutual information, average gradient, standard deviation, spatial frequency, etc. From the analysis, this paper concludes that the proposed algorithm improved gradient information in the fused image by 35.7%, entropy by 5.7%, spatial frequency by 32.7%, edge strength by 14.5%, and minimized the information loss by 43.6%. Therefore, the novel method of weight map computation produces a detailed and noise-free image, which is helpful for better diagnosis in clinical applications. [ABSTRACT FROM AUTHOR]

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship is established in local and non-local fields to improve the model perception ability. Secondly, the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features, and the cross-scale interactive feature enhancement module (CIFEM) is used to enhance the attention ability of the fine-grained features. Thirdly, the Cross-scale Interactive Feature fusion FPN network (CIF-FPN) is constructed to realize bidirectional interactive fusion between deep features and shallow features, and the low-level features are enhanced in deep semantic features. Finally, 4 ablation experiments, 3 comparison experiments of detection, 3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets. The results showed that APdet, APseg, ARdet and ARseg indexes are improved by 5.5%, 5.15%, 3.11% and 6.79% compared with Mask RCNN (resnet50). Based on the above research, the precise detection and segmentation of the lesion region are realized in this paper. This method has positive significance for the detection of lung tumors. [ABSTRACT FROM AUTHOR]

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience. Approach. To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning. Main results. To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs. Significance. The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal Multimedia Tools and Applications' tops the list of journals with a total of 29 published articles. [ABSTRACT FROM AUTHOR]

Key Clinical Message: The phenomenon of vessel pedicle ossification is a noteworthy aspect of the repair and reconstruction of maxillofacial defects. Imaging findings typically reveal high‐density shadows within the vascular pedicle pathway, which may be managed through conservative observation or surgical intervention as deemed appropriate. Vessel pedicle ossification is a relatively uncommon complication associated with the reconstruction of oral and maxillofacial tissue defects using free tissue flap repair. In this paper, we report a case of pedicle ossification and conduct a comprehensive review of previous literature. A 39‐year‐old man presented with a limited ability to open his mouth 6 months after fibular flap reconstruction of the mandible. Plain film X‐ray and computed tomography (CT) indicated pedicle ossification. Two years after the initial operation, the restriction in the patient's ability to open his mouth had not worsened, although there were more pronounced radiographic abnormalities. [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]

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC. [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]

In this paper, we propose the incorporation of noisy image patches and the impulse response of a low-pass filter (LPF) in a convolutional neural network (CNN) to denoise Poisson--Gaussian noise in low-dose computed tomography (LDCT) images. The approach is referred to as fast and flexible denoising CNN (FFDNet)-impulse response (FFDNet-IR) in this paper. The power spectrum sparsity LPF (SLPF) allows low-frequency components to pass through while suppressing higher frequency components by the sparsity approach of the power spectrum, and it is employed to determine the impulse response of LPF. Three well-known types of LPF, namely, Direct LPF, Gaussian LPF, and Butterworth LPF, are also considered to obtain the impulse response of LPF. In the FFDNet-IR, both the noisy image patches and the IR of the LPF are sequentially inputted into the FFDNet to eliminate the Poisson--Gaussian noise. This approach enhances the denoising performance in LDCT images compared with the conventional FFDNet in the evaluation metrics of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM). Moreover, the FFDNet-IR trained with the Poisson--Gaussian noise model demonstrates the generalization ability and effectively eliminates only Poisson or Gaussian noise. The experiments indicate that the FFDNet-IR more effectively suppresses the noise artifacts and preserves image details compared with the baseline FFDNet, as well as traditional methods such as block-matching and 3D filtering (BM3D) and nonlocal mean (NLM) for LDCT image denoising. [ABSTRACT FROM AUTHOR]

Signal‐to‐noise ratio and spatial resolution are quantitatively analysed in the context of in‐line (propagation based) X‐ray phase‐contrast imaging. It is known that free‐space propagation of a coherent X‐ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal‐to‐noise in the resultant images without deteriorating the spatial resolution. This results in violation of the noise‐resolution uncertainty principle and demonstrates 'unreasonable' effectiveness of the method. On the other hand, when the process of free‐space propagation is performed in software, using the detected intensity distribution in the object plane, it cannot reproduce the same effectiveness, due to the amplification of photon shot noise. Here, it is shown that the performance of Paganin's method is determined by just two dimensionless parameters: the Fresnel number and the ratio of the real decrement to the imaginary part of the refractive index of the imaged object. The relevant theoretical analysis is performed first, followed by computer simulations and then by a brief test using experimental images collected at a synchrotron beamline. More extensive experimental tests will be presented in the second part of this paper. [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]

Trauma and orthopedic surgery commonly rely on intraoperative radiography or fluoroscopy, which are essential for visualizing patient anatomy and safely completing surgical procedures. However, these imaging methods generate ionizing radiation, which in high doses carries a potential health risk to patients and operating personnel. There is an established need for formal training in obtaining precise intraoperative imaging while minimizing radiation exposure. Virtual reality (VR) simulation serves as a promising tool for orthopaedic trainees to develop skills in safe intraoperative imaging, without posing harm to patients, operating room staff, or themselves. This paper aims to provide a brief overview of literature surrounding VR training for intraoperative imaging in orthopaedic surgery. In addition, we discuss areas for improvement and future directions for development in the field. [ABSTRACT FROM AUTHOR]

Pseudomyxoma peritonei is a rare pathological condition characterized by mucinous tumor tissue implants on the peritoneal surface. Although the cause of Pseudomyxoma peritonei has been extensively studied, the prevailing agreement is that it stems from mucinous tumors that occur in the ovaries or appendix. The tumor tissue typically remains localized to the peritoneum and does not exhibit extraperitoneal spread. Patients with Pseudomyxoma peritonei may present with symptoms such as abdominal pain, bloating, loss of appetite, and shortness of breath. Computerized Tomography is commonly used for diagnostic purposes. The treatment of Pseudomyxoma peritonei typically involves surgical evacuation of the tumoral tissue, followed by cytoreduction and Hyperthermic Intraperitoneal Chemotherapy. While effective treatment options are available, some patients may require repeated surgeries over an extended period. This paper reports on a case study of a patient with a history of recurrent Pseudomyxoma peritonei, necessitating multiple surgical interventions over a decade. The paper concludes with a review of the relevant literature. [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]

Artificial Intelligence (AI) is a field which has spread its wing everywhere. The two subsets of AI – Machine Learning (ML) and Deep Learning (DL) are very much in demand nowadays. Use of AI in healthcare is now seen at a large scale. One of the prominent uses of AI algorithm is in detection of cancer. The number of cancer cases is increasing rapidly and both doctors and researchers are trying to find a solution for the same. Detection of cancer is possible through various tests like MRI, CT scan, etc. It is a challenge for the doctors to interpret the tests correctly as it is very complex and time-consuming. It becomes even more challenging to infer the tests if the cancer is in the early stage. This is where AI can help. Various ML/ DL algorithms can be used to speed up the detection process and thus identify cancerous cells in the initial stage so that the survival rate of the patient increases. Early diagnosis and timely intervention can make a significant difference between life and death for cancer patients. This paper briefly outlines the role of AI including ML and DL in cancer detection and how various researchers are using it for prediction of disease. The paper also outlines the general steps used in AI for detection of cancer. [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]

The issue of preserving carbonatic stones of cultural heritage (CH) restored in the past that have undergone new decay phenomena is strongly emerging and conservation science has not yet found a reliable solution. In this paper, we propose the application of synchrotron radiation X-ray diffraction computed tomography (XRDCT) to explore the effects of using inorganic-mineral products (ammonium oxalate; ammonium phosphate) in sequence as a novel, compatible and effective re-treatment approach to consolidate decayed carbonatic stones already treated with inorganic-mineral treatments. High-quality XRDCT datasets were used to qualitatively/quantitatively investigate and 3D localize the complex mixture of crystalline phases formed after the conservation re-treatments within a porous carbonatic stone substrate. The XRDCT reconstruction images and the structural refinements of XRD patterns with the Rietveld methods showed that the phase composition of reaction products, their volume distribution, and weight fraction vary as a function of the treatment sequence and penetration depth. The high potential of XRDCT allows (i) assessment of peculiar trends of each treatment/treatment sequence; (ii) exploration of the reaction steps of the sequential treatments and (iii) demonstration of the consolidating effect of inorganic re-treatments, non-destructively and at the micron scale. Above all, our study (i) provides new analytical tools to support the conservation choices, (ii) showcases new analytical possibilities for XRDCT in conservation science, including in investigations of CH materials and decay processes, and (iii) opens up new perspectives in analytical chemistry and material characterisation for the non-destructive and non-invasive analysis of reactions within heterogeneous polycrystalline systems. [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]

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions. [ABSTRACT FROM AUTHOR]

Background: Root canal therapy is one of the main treatments for root canal diseases, and effective irrigation is the key to successful treatment. Side-vented needle is one of the commonly used needle types in clinic. In the real root canal, due to the influence of the curvature of the root canal, the irrigation flow field in different needle directions shows obvious differences. At the same time, changes in root canal curvature and working depth will lead to changes in irrigation efficiency and the flow field. Both the mainstream of the irrigation flow and the shear stress near the wall changes significant. Consequently, either the replacement in the root canal or the removal efficiency of the smear layers is apparently modified. Materials and methods: In this paper, the permanent root canal of the maxillary first molar prepared until 15/04 were scanned by micro-CT, and then imported into the software for 3D reconstruction. The key parameters of flushing efficiency of 30G side needle at different working depths of 4.75 mm, 5 mm, 5.25 mm and 5.5 mm were compared. Meanwhile, the simulated models with different curvatures of 0°, 5°, 10°, 20° and 30° based on the real root canal were reconstructed to investigate the curvature effect on the irrigation efficiency. Results: The results show that moderate working depth (such as 4.75 mm and 5.25 mm in present paper) helps to improve the replacement capacity of irrigation flow. At the same time, the apical pressure decreased as the working depth increased. The curvature of the root canal seriously affects the removal depth of the smear layers of the root canal. A root canal with a large curvature (especially 20° and 30°) can significantly improve the difficulty of irrigation. Conclusions: (1) Moderate working depth helps to improve the displacement capacity, the ERD of the irrigation flow is generally improved at the working depths of 4.75 mm and 5.25 mm, and the apical pressure will decrease with the increase of working depth. (2) The large curvature of the root canal can significantly improve the difficulty of irrigation. The curvature of the root canal can severely influence the removal depth of the smear layer on the wall. It can be found both the span and the depth of the ESS for little curvatures (5° and 10°) root canals are higher than those for large curvatures (20° and 30°). [ABSTRACT FROM AUTHOR]

A control problem for the heating of moist paperboard with the evaporation of moisture from the inner pore surface is considered. The microstructure of the paperboard is known from the CT images. It is easily parametrized w.r.t. the volume fraction of the pores, the surface area of the pores related to the unit volume, averaged area of the contact surfaces between two fibers and the unsupported fiber length between two contact nodes, fiber thickness, and tortuosity. Simple averaging formulas are provided for the computation of the effective coefficients in the coupled diffusion‐heat problem and the dissipation energetic balance was analyzed and we pass to the limit w.r.t. the small parameter, the relation between the pore size and the paper‐board thickness. The limit model is similar to those models, recently available in the literature. However, it provides more understanding and different limiting models and the thresholds for them. Also, the limitation on the heating is derived from the solvability criteria, which means, that the evaporation should not overtake the heating. The model is validated by experimental and numerical results found in the literature. [ABSTRACT FROM AUTHOR]

C/SiC composites are high-temperature structural materials with excellent properties, such as high-temperature resistance and light weight, and are widely used in aerospace fields as advanced materials. Pores are common and unavoidable defects inside composite components, especially affecting mechanical properties and further affecting the safety and life of the components during the service stage. Conventional methods for pore characterization in regular-shaped composites are not suitable for special-shaped C/SiC composite components. To accurately evaluate the effect of pore defects on component properties, in this paper, an effective method is proposed to characterize the spatial distribution state of isolated pores in a special-shaped C/SiC composite component. The isolated pores in the C/SiC composites are identified and localized based on the fractal dimension of local fine characterization of X-ray computed tomography (CT) images using fractal theory. A very strong correlation is found between the fractal dimension of the pore segmentation images and pore characteristics, which is used to identify the connected and isolated pores in C/SiC composites. According to the CT images and fractal dimension statistics analysis results, isolated pores were accurately identified and located. The nondestructive evaluation of isolated pore distribution state inside the studied component was achieved via three-dimensional visual characterization. The minimum volume of the isolated pores identified by the proposed method is 5832 μm3. The results provide a strong basis for further analyzing the effect of pore defect distributions on the mechanical properties of composite components. [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]