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Children living with achondroplasia are at an increased risk of developing neurological complications, which may be associated with acute and life-altering events. To remediate this risk, the timely acquisition of effective neuroimaging that can help to guide clinical management is essential. We propose imaging protocols and follow-up strategies for evaluating the neuroanatomy of these children and to effectively identify potential neurological complications, including compression at the cervicomedullary junction secondary to foramen magnum stenosis, spinal deformity and spinal canal stenosis. When compiling these recommendations, emphasis has been placed on reducing scan times and avoiding unnecessary radiation exposure. Standardized imaging protocols are important to ensure that clinically useful neuroimaging is performed in children living with achondroplasia and to ensure reproducibility in future clinical trials. The members of the European Society of Pediatric Radiology (ESPR) Neuroradiology Taskforce and European Society of Neuroradiology pediatric subcommittee, together with clinicians and surgeons with specific expertise in achondroplasia, wrote this opinion paper. The research committee of the ESPR also endorsed the final draft. The rationale for these recommendations is based on currently available literature, supplemented by best practice opinion from radiologists and clinicians with subject-specific expertise. [ABSTRACT FROM AUTHOR]

Carbon paper is commonly used to fabricate electrodes for batteries, and its morphology is crucial to the internal mass transport. In this work, geometric models of carbon paper are obtained by experimental and numerical reconstruction methods. The micromorphology of the carbon paper is obtained with a scanning electron microscope and an X-ray computed tomography scanner, and the binary slicing method is used in the experimental reconstruction method. Three different methods are used for numerical reconstruction, namely the layered 2D fibre, 3D fibre stacking and layered 3D fibre stacking methods. The structure and characteristic parameters of the carbon paper, such as pore size distribution, dimensionless specific surface area, effective diffusion coefficient and anisotropic coefficient, are statistically analysed for comparison. The dimensionless effective diffusion coefficients of Li+ in different directions in the electrolyte-filled carbon paper are obtained using lattice Boltzmann method. Results show that the internal structural features directly affect mass transport. The curves of the calculated effective diffusivity versus porosity are well fitted using a power function similar to Bruggeman equation within the porosity range of 0.66–0.86. The anisotropic coefficient is obtained from the effective diffusion coefficient in different directions. [ABSTRACT FROM AUTHOR]

Ultrasound practice is a longstanding tradition for radiology departments, being part of the family of imaging techniques. Ultrasound is widely practiced by non-radiologists but becoming less popular within radiology. The position of ultrasound in radiology is reviewed, and a possible long-term solution to manage radiologist expectations is proposed. An international group of experts in the practice of ultrasound was invited to describe the current organisation of ultrasound within the radiology departments in their own countries and comment on the interaction with non-radiologists and training arrangements. Issues related to regulation, non-medical practitioners, and training principles are detailed. A consensus view was sought from the experts regarding the position of ultrasound within radiology, with the vision of the best scenario for the continuing dominance of radiologists practising ultrasound. Comments were collated from nine different countries. Variable levels of training, practice, and interaction with non-radiologist were reported, with some countries relying on non-physician input to manage the service. All experts recognised there was a diminished desire to practice ultrasound by radiologists. Models varied from practising solely ultrasound and no other imaging techniques to radiology departments being central to the practice of ultrasound by radiologists and non-radiologist, housed within radiology. The consensus view was that the model favoured in select hospitals in Germany would be the most likely setup for ultrasound radiologist to develop and maintain practice. The vision for 20 years hence is for a central ultrasound section within radiology, headed by a trained expert radiologist, with non-radiologist using the facilities. Critical relevance statement The future of ultrasound within the radiology department should encompass all ultrasound users, with radiologists expert in ultrasound, managing the ultrasound section within the radiology department. The current radiology trainees must learn of the importance of ultrasound as a component of the 'holistic' imaging of the patient. Key points: 1. Ultrasound imaging within radiology departments precedes the introduction of CT and MR imaging and was first used over 50 years ago. 2. Non-radiology practitioners deploy ultrasound examinations to either 'problem solve' or perform a comprehensive ultrasound examination; radiologists provide comprehensive examinations or use ultrasound to direct interventional procedures. 3. Radiology does not 'own' ultrasound, but radiologists are best placed to offer a comprehensive patient-focused imaging assessment. 4. A vision of the future of ultrasound within the radiology department is encompassing all ultrasound users under radiologists who are experts in ultrasound, positioned within the radiology department. 5. The current radiology trainee must be aware of the importance of ultrasound as a component of the 'holistic' imaging of the patient. [ABSTRACT FROM AUTHOR]

The introduction of smaller footprint, more sensitive Cadmium-Zinc-Telluride (CZT)-based detectors with improved spatial and energy resolution has enabled the design of innovative full-ring 360° CZT SPECT/CT systems (e.g., VERITON® and StarGuide™). With this transformative technology now aiming to become mainstream in clinical practice, several critical questions need to be addressed. This EANM position paper provides practical recommendations on how to use these devices for routine bone SPECT/CT studies, facilitating the transition from traditional planar whole-body imaging and conventional SPECT/CT to these novel systems. In particular, initial guidance is provided on imaging acquisition and reporting workflows, image reconstruction, and CT acquisition parameters. Given the emerging nature of this technology, the available evidence base is still limited, and the proposed adaptations in workflows and scan protocols will likely evolve before being integrated into definitive guidelines. In the meantime, this EANM position paper serves as a comprehensive guide for integrating these advanced hybrid SPECT/CT imaging systems into clinical practice and outlining areas for further study. [ABSTRACT FROM AUTHOR]

We admit, however, that fracture mechanical principles and rock physics elements behind rock fracturing and fault activation may be similar, and that the joint discussion of fracturing and faulting, therefore, has a long tradition in the rock mechanics and rock engineering community. 1 Participants of the '13th EURO-Conference on Rock Physics and Geomechanics - The Guéguen Conference' during the social event at the Winzerberg Potsdam This special issue consists of 19 scientific papers which are based on these conference contributions and all of which address different important aspects relevant to the variety of industrial applications outlined above. This special issue is dedicated to our dear colleague and friend Prof. Ove Stephansson, who was not only one of Sweden's most influential geologists, but also an enthusiastic supporter of the 13th EURO-Conference on Rock Physics and Geomechanics. Special Issue on Rock Fracturing and Fault Activation: Experiments and Models: Selected Papers Presented at the 13th EURO-Conference on Rock Physics and Geomechanics - The Guéguen Conference Held on 2-6 September 2019 in Potsdam, Germany. [Extracted from the article]

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques. [ABSTRACT FROM AUTHOR]

Objectives: To develop and evaluate methods for assembling radiopaque printed paper sheets to realistic patient phantoms for CT dose and image quality testing.Methods: CT images of two patients were radiopaque printed with aqueous potassium iodide solution (0.6 g/ml) on paper. Two methods were developed for assembling the paper sheets to head and neck phantoms. (1) Printed sheets were fed to a paper-based 3D printer along with corresponding 3D printable STL files. (2) Paper stacks of 5-mm thickness were glued with toner, cut to the patient shape and assembled to a phantom. In a sample application study, both phantoms were examined with five different tube current settings. Images were reconstructed using filtered-back projection (FBP) and iterative reconstruction (AIDR 3D) with three strength levels. Dose length product (DLP), signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNRs) were analysed. Data were analysed using 2-way analysis of variance (ANOVA).Results: Both methods achieved anthropomorphic phantoms with detailed patient anatomy. The 3D printer yielded a precise reproduction of the external patient shape, but caused visible glue artefacts. Gluing with toner avoided these artefacts and yielded more flexibility with regard to phantom size. In the sample application study, non-inferior SNR and CNR and up to 83.7% lower DLP were achieved on the phantoms with AIDR 3D compared with FBP.Conclusions: Two methods for assembling radiopaque printed paper sheets to phantoms of individual patients are presented. The sample application demonstrates potential for simulation of patient imaging and systematic CT dose and image quality assessment.Key Points: • Two methods were developed to create realistic CT phantoms of individual patients from radiopaque printed paper sheets. • Analysis of five tube current and four reconstruction settings on two radiopaque 3D printed patient phantoms yielded non-inferior SNR and CNR and up to 83.7% lower dose with iterative reconstruction in comparison with filtered back projection. • Radiopaque 3D printed phantoms can simulate patients and allow systematic analysis of CT dose and image quality parameters. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDA) is the third leading cause of cancer-related death in the United States and is projected to be the second by 2030. Systemic combination chemotherapy is considered an essential first-line treatment for the majority of patients with PDA, in both the neoadjuvant and palliative settings. In addition, a number of novel therapies are being tested in clinical trials for patients with advanced PDA. In all cases, accurate and timely assessment of treatment response is critical to guide therapy, reduce drug toxicities and cost from a failing therapy, and aid adaptive clinical trials. Conventional morphological imaging has significant limitations, especially in the context of determining primary tumor response and resectability following neoadjuvant therapies. In this article, we provide an overview of current therapy options for PDA, highlight several morphological imaging findings that may be helpful to reduce over-staging following neoadjuvant therapy, and discuss a number of emerging imaging, and non-imaging, tools that have shown promise in providing a more precise quantification of disease burden and treatment response in PDA. [ABSTRACT FROM AUTHOR]

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]

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices. [ABSTRACT FROM AUTHOR]

This expert opinion paper on cardiac imaging after acute ischemic stroke or transient ischemic attack (TIA) includes a statement of the "Heart and Brain" consortium of the German Cardiac Society and the German Stroke Society. The Stroke Unit-Commission of the German Stroke Society and the German Atrial Fibrillation NETwork (AFNET) endorsed this paper. Cardiac imaging is a key component of etiological work-up after stroke. Enhanced echocardiographic tools, constantly improving cardiac computer tomography (CT) as well as cardiac magnetic resonance imaging (MRI) offer comprehensive non- or less-invasive cardiac evaluation at the expense of increased costs and/or radiation exposure. Certain imaging findings usually lead to a change in medical secondary stroke prevention or may influence medical treatment. However, there is no proof from a randomized controlled trial (RCT) that the choice of the imaging method influences the prognosis of stroke patients. Summarizing present knowledge, the German Heart and Brain consortium proposes an interdisciplinary, staged standard diagnostic scheme for the detection of risk factors of cardio-embolic stroke. This expert opinion paper aims to give practical advice to physicians who are involved in stroke care. In line with the nature of an expert opinion paper, labeling of classes of recommendations is not provided, since many statements are based on expert opinion, reported case series, and clinical experience. [ABSTRACT FROM AUTHOR]

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user's ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient's images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges. [ABSTRACT FROM AUTHOR]

Lung cancer is the most frequently fatal cancer, with poor survival once the disease is advanced. Annual low-dose computed tomography has shown a survival benefit in screening individuals at high risk for lung cancer. Based on the available evidence, the European Society of Radiology and the European Respiratory Society recommend lung cancer screening in comprehensive, quality-assured, longitudinal programmes within a clinical trial or in routine clinical practice at certified multidisciplinary medical centres. Minimum requirements include: standardised operating procedures for low-dose image acquisition, computer-assisted nodule evaluation, and positive screening results and their management; inclusion/exclusion criteria; expectation management; and smoking cessation programmes. Further refinements are recommended to increase quality, outcome and cost-effectiveness of lung cancer screening: inclusion of risk models, reduction of effective radiation dose, computer-assisted volumetric measurements and assessment of comorbidities (chronic obstructive pulmonary disease and vascular calcification). All these requirements should be adjusted to the regional infrastructure and healthcare system, in order to exactly define eligibility using a risk model, nodule management and a quality assurance plan. The establishment of a central registry, including a biobank and an image bank, and preferably on a European level, is strongly encouraged. Key points: • Lung cancer screening using low dose computed tomography reduces mortality. • Leading US medical societies recommend large scale screening for high-risk individuals. • There are no lung cancer screening recommendations or reimbursed screening programmes in Europe as of yet. • The European Society of Radiology and the European Respiratory Society recommend lung cancer screening within a clinical trial or in routine clinical practice at certified multidisciplinary medical centres. • High risk, eligible individuals should be enrolled in comprehensive, quality-controlled longitudinal programmes. [ABSTRACT FROM AUTHOR]

This paper focuses on how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices. To achieve this, we present a comparative overview of the United States (USA), European Union (EU), and China. Our search in the governmental database identified 59 AI medical devices approved in China as of July 2023. In comparison to the rules-based regulatory approach in China, the approaches in the USA and EU are more standards-oriented. [ABSTRACT FROM AUTHOR]

This paper proposes a method for obtaining adaptive and high-precision surface meshes directly from Industrial computed tomography (ICT) projection data. Firstly, an adaptive volume octree is recursively constructed from top to bottom using a two-stage geometric error metric function. The CT values and gradient values at the nodes are computed using the Feldkamp–Davis–Kress (FDK) reconstruction algorithm and its derivatives, achieving sub-voxel precision. Next, feature vertices are calculated based on Quadratic error functions (QEFs), and a dual mesh is constructed. Finally, Hermite interpolation is used to determine the iso-surface vertices, and the Convex Contouring lookup table is employed to accurately extract the iso-surface contours, resulting in high-precision and crack-free surface meshes. Experimental results show that the surface meshes generated by the proposed method exhibit superior dimensional accuracy, form and position accuracy, and surface model accuracy compared to traditional methods, and the dimensional accuracy has been enhanced by approximately 10–30%. [ABSTRACT FROM AUTHOR]

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches. [ABSTRACT FROM AUTHOR]

Objective: To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention. Methods: The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords. Results: A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the "European Radiology" (181 articles, h-index = 46), followed by the "European Journal of Radiology" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest. Conclusion: Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future. Critical relevance statement: This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice. Key Points: Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications. [ABSTRACT FROM AUTHOR]

Amid the coronavirus disease 2019 (COVID-19) pandemic, numerous publications of imaging findings in children have surfaced in a very short time. Publications discuss populations of overlapping age groups and describe different imaging patterns. We aim to present an overview of the quantity and type of literature available regarding COVID-19 chest imaging findings in children according to a 2020 publication timeline. We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. We searched terminology related to COVID-19, chest, children and imaging modalities in PubMed and Embase. The included papers were published online in 2020 and described imaging findings specific to children and reported five or more cases. Two researchers reviewed each abstract to determine inclusion or exclusion, and a radiologist reconciled any disagreements. Then we reviewed full articles for the main analysis. Eligible study designs included original articles, case series (≥5 cases), systematic reviews and meta-analyses. We excluded non-English manuscripts, retracted articles, and those without available full text. The remaining articles were distributed to four pediatric radiologists (on the Society for Pediatric Radiology Thoracic Committee), who summarized chest imaging findings. Eighty-two articles were included in the final analysis - 28% in radiology journals and 71% in non-radiology journals; 71% contained original data and 29% were review-style papers. There was a disproportionate contribution of review-style papers in April (55%), considering the paucity of preceding publications with original data in March (5 papers). June had the highest number of publications (n=14), followed by April (n=11) and July (n=11). Most (52%) original papers were from China and most individual pediatric imaging descriptions were from China (57%), while the majority of review papers (83%) were international. Imaging descriptions were available for 2,199 children (1,678 CT descriptions and 780 chest radiography descriptions). Findings included a 25% normal CT scan reports vs. 40% normal chest radiography reports. Ground-glass opacification was the most common CT finding (33%) and was reported in only a minority of chest radiographs (9%). A significant amount of information on pediatric COVID-19 chest imaging has become rapidly available over a short period. Most publications in 2020 were original articles, but they were published more often in non-radiology journals. A disproportionate number of review articles were published early on and were based on little original pediatric imaging data. CT scan reports, which represent the standard, outnumbered radiographic reports and indicated that ground-glass opacification is the main imaging finding and that only a quarter of scans are normal in children with COVID-19. [ABSTRACT FROM AUTHOR]

Purpose: The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT. Methods: We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction. Results: Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality. Conclusion: DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images. [ABSTRACT FROM AUTHOR]

3527039 16 Sadeghi M, Saedi B, Ghaderi Y. Endoscopic management of contact point headache in patients resistant to medical treatment. As it seems clear, we conducted a meta-analysis study to evaluate the symptomatological improvements obtained after surgical treatment for rhinogenic headache [[1]], comparing the results reported with a control group treated with medical therapy. Dear Editor, We are first of all pleased to have allowed us to comment on the Letter to the editor regarding our paper entitled "Endoscopic surgical treatment for rhinogenic contact point headache: systematic review and meta-analysis" [[1]]. 10.1097/SCS.0000000000004211 10 Wang J, Yin JS, Peng H. Investigation of diagnosis and surgical treatment of mucosal contact point headache. [Extracted from the article]

A review is presented of Management Guide for Incidental Findings on Computed Tomography and Magnetic resonance imaging App by RADIOLOGiQ.

Automatic segmentation of the liver in abdominal CT images is critical for guiding liver cancer biopsies and treatment planning. Yet, automatic segmentation of CT liver images remains challenging due to the poor contrast between the liver and surrounding organs in abdominal CT images. In this paper, we propose a novel network for liver segmentation, and the network is essentially a U-shaped network with an encoder–decoder structure. Firstly, the complementary feature enhancement unit is designed in the network to mitigate the semantic gap between encoder and decoder. The complementary feature enhancement unit is based on subtraction, which enhances the complementary features between encoder and decoder. Secondly, this paper proposes a new cross attention model that no longer generates value by convolution, which reduces redundant information and enhances the contextual information of single sparse attention by encoding contextual information by 3 × 3 convolution. The dice score, accuracy, and precision of our network on the LiTS dataset were 95.85 % , 97.19 % , and 97.11 % , and the dice score, accuracy, and precision on the dataset consisted of 3Dircadb and CHAOS were 93.65 % , 94.38 % , and 97.53 % . [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learning-based denoising methods are built on convolutional neural networks (CNNs), which concentrate on local information and have little capacity for multiple structures modeling. Transformer structures are capable of computing each pixel's response on a global scale, but their extensive computation requirements prevent them from being widely used in medical image processing. To reduce the impact of LDCT scans on patients, this paper aims to develop an image post-processing method by combining CNN and Transformer structures. This method can obtain a high-quality images from LDCT. A hybrid CNN-Transformer (HCformer) codec network model is proposed for LDCT image denoising. A neighborhood feature enhancement (NEF) module is designed to introduce the local information into the Transformer's operation, and the representation of adjacent pixel information in the LDCT image denoising task is increased. The shifting window method is utilized to lower the computational complexity of the network model and overcome the problems that come with computing the MSA (Multi-head self-attention) process in a fixed window. Meanwhile, W/SW-MSA (Windows/Shifted window Multi-head self-attention) is alternately used in two layers of the Transformer to gain the information interaction between various Transformer layers. This approach can successfully decrease the Transformer's overall computational cost. The AAPM 2016 LDCT grand challenge dataset is employed for ablation and comparison experiments to demonstrate the viability of the proposed LDCT denoising method. Per the experimental findings, HCformer can increase the image quality metrics SSIM, HuRMSE and FSIM from 0.8017, 34.1898, and 0.6885 to 0.8507, 17.7213, and 0.7247, respectively. Additionally, the proposed HCformer algorithm will preserves image details while it reduces noise. In this paper, an HCformer structure is proposed based on deep learning and evaluated by using the AAPM LDCT dataset. Both the qualitative and quantitative comparison results confirm that the proposed HCformer outperforms other methods. The contribution of each component of the HCformer is also confirmed by the ablation experiments. HCformer can combine the advantages of CNN and Transformer, and it has great potential for LDCT image denoising and other tasks. [ABSTRACT FROM AUTHOR]

As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives. [ABSTRACT FROM AUTHOR]

The fusion of multimodal medical images has garnered painstaking attention for clinical diagnosis and surgical planning. Although various scholars have designed numerous fusion methods, the challenges of extracting substantial features without introducing noise and non-uniform contrast hindered the overall quality of fused photos. This paper presents a multimodal medical image fusion (MMIF) using a novel deep convolutional neural network (D-CNN) along with preprocessing schemes to circumvent the mentioned issues. A non-linear average median filtering (NL-AMF) and multiscale improved top-hat (MI-TH) approach are utilized at the preprocessing stage to remove noise and improve the contrast of images. The non-linear anisotropic diffusion (NL-AD) scheme is employed to split the photos into base and detailed parts. The fusion of base parts is accomplished by a dimension reduction method to retain the energy information. In contrast, the detailed parts are fused by novel D-CNN to preserve the enriched detailed features effectively. The simulation results demonstrate that the proposed method produces better brightness contrast and more image details than existing methods by acquiring 0.7649 to 0.8986, 0.3520 to 0.4783, 0.7639 to 0.9056, 68.8932 to 81.0487 gain for quality transfer ratio from source photo to a generated photo ( Q G AB ), feature mutual information (FMI), structural similarity index (SSIM), and average pixel intensity (API) respectively. [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]

In an era in which cardiovascular disease has become the main cause of death all over the world, diagnostic accuracy in identifying blood vessels has become particularly important. Vascular stenosis causes serious health risks by affecting blood flow, leading to conditions like heart attacks and strokes. Traditional diagnostic methods face challenges in terms of timeliness and accuracy. Our systematic review aims to critically assess the role of machine learning (ML) techniques in enhancing computed tomography angiography's (CTA) diagnostic capabilities for vasoconstriction. This review followed the predetermined inclusion and exclusion criteria and focused on research articles published between January 2013 and October 2023 collected from databases such as PubMed, IEEE, Web of Science, and Scopus. Studies focus on multiphase CTA or dynamic CTA; papers do not use the ML; and papers not in English are removed. The risk of bias of included studies was evaluated using the QUADAS2 tool. The results were analyzed in tabular form using metrics such as accuracy, sensitivity, and specificity and examine variations in stenosis detection by anatomical regions. In our review, a total of 63 studies were identified as relevant. These studies included a variety of ML applications for identifying anatomical stenosis of the arteries in different anatomical areas. The findings highlighted a trend of high sensitivity and specificity in broader anatomical assessments, with nuanced variations observed in detailed segmental analysis. The review acknowledges limitations within the existing studies, including the retrospective nature of most studies and their limited scope in terms of patient diversity and center variation. Nonetheless, the implications of integrating ML in vascular stenosis detection via CTA are profound, suggesting a pivotal shift toward more accurate, efficient, and patient-centric diagnostic practices in cardiovascular care. Registration: The protocol for this systematic review and meta-analysis was registered on PROSPERO, with the registration number CRD420234603. [ABSTRACT FROM AUTHOR]

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture. [ABSTRACT FROM AUTHOR]

Recently in diagnosis of Aortic dissection (AD), the synthesis of contrast enhanced CT (CE-CT) images from non-contrast CT (NC-CT) images is an important topic. Existing methods have achieved some results but are unable to synthesize a continuous and clear intimal flap on NC-CT images. In this paper, we propose a multi-stage cascade generative adversarial network (MCGAN) to explicitly capture the features of the intimal flap for a better synthesis of aortic dissection images. For the intimal flap with variable shapes and more detailed features, we extract features in two ways: dense residual attention blocks (DRAB) are integrated to extract shallow features and UNet is employed to extract deep features; then deep features and shallow features are cascaded and fused. For incomplete flaps or lack of details, we use spatial attention and channel attention to extract key features and locations. At the same time, multi-scale fusion is used to ensure the continuity of the intimal flap. We perform the experiment on a set of 124 patients (62 with AD and 62 without AD). The evaluation results show that the synthesized images have the same characteristics as the real images and achieves better results than the popular methods. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has proven to be important for computed tomography (CT) image denoising. However, such models are usually trained under supervision, requiring paired data that may be difficult to obtain in practice. Diffusion models offer unsupervised means of solving a wide range of inverse problems via posterior sampling. In particular, using the estimated unconditional score function of the prior distribution, obtained via unsupervised learning, one can sample from the desired posterior via hijacking and regularization. However, due to the iterative solvers used, the number of function evaluations (NFE) required may be orders of magnitudes larger than for single-step samplers. In this paper, we present a novel image denoising technique for photon-counting CT by extending the unsupervised approach to inverse problem solving to the case of Poisson flow generative models (PFGM)++. By hijacking and regularizing the sampling process we obtain a single-step sampler, that is NFE = 1. Our proposed method incorporates posterior sampling using diffusion models as a special case. We demonstrate that the added robustness afforded by the PFGM++ framework yields significant performance gains. Our results indicate competitive performance compared to popular supervised, including state-of-the-art diffusion-style models with NFE = 1 (consistency models), unsupervised, and non-DL-based image denoising techniques, on clinical low-dose CT data and clinical images from a prototype photon-counting CT system developed by GE HealthCare. [ABSTRACT FROM AUTHOR]

Background: Dysgeusia or altered taste is a rare complication following suspension microlaryngoscopy with the incidence ranging from 2.9 to 12.1%. We report this with recommendations to avoid similar complications following suspension laryngoscopy, tonsillectomy, and tongue base surgery which require pressure to be placed on the tongue for better surgical field visualization. Case presentation: A 53-year-old man with underlying diabetes mellitus presented with long standing history of irritative cough, globus sensation, and evidence of laryngopharyngeal reflux. A left ventricle swelling was noted on flexible laryngoscopy and neck. He underwent direct laryngoscopy and biopsy of the left ventricle lesion which revealed to be acute on chronic inflammation. Day 1 postoperatively, patient complained of reduced sensation over left hemi-tongue. A referral to a neurologist was made for further examination and he was found to have loss of taste over anterior two-third of tongue and treated with vitamin B complex. To date, dysgeusia remains persistent. Patient relayed his grievances to the hospital following these complications. Review of literature on relevant topic was made through PubMed, Web of Science, and Cochrane Library Database by two reviewers, working independently. Twenty-three papers, available in full, written in English language, containing number of cases, type of surgery, and complications were extracted and studied. Conclusions: Risk of dysgeusia and paresthesia postoperatively should be informed during consent taking for all patients undergoing tonsillectomy, laryngeal microsurgery, and tongue base surgery. Zinc deficiency should be investigated in patients with persistent taste disturbance post-tonsillectomy. Intermittent release during suspension laryngoscopy beyond 30 min to reduce post-surgical complications. Greater care should be taken to reduce the amount of force during the suspension laryngoscopy due to smaller oral and oropharyngeal structure in female. Keeping close to the tonsillar capsule particularly in mid and lower pole areas should be done in tonsillectomy using diathermy dissection. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content. This paper demonstrates that an algorithm designed for video augmentation can be efficiently used for 3D tomography reconstruction across the materials science and medical domain, namely across disciplines and length-scales. [ABSTRACT FROM AUTHOR]

Background: To evaluate the feasibility and benefits of digitized informed patient consent (D-IPC) for contrast-enhanced CT and compare digitized documentation with paper-based, conventional patient records (C-PR). Methods: We offered D-IPC to 2016 patients scheduled for a CT. We assessed patient history (e.g., CT examinations, malignant or cardiovascular diseases) and contraindications (red flags) for a CT (e.g., thyroid hyperfunction, allergies) using a tablet device. We evaluated the success rate of D-IPC and compared patient age between the subgroups of patients who were able or unable to complete D-IPC. We analyzed the prevalence of marked questions and red flags (RF). RF were compared with the documentation from C-PR. We estimated greenhouse gas (GHG) emissions for paperless workflow and provide a cost–benefit analysis. Results: Overall, 84.4% of patients completed D-IPC. They were younger (median 61 years) than unsuccessful patients (65 years; p < 0.001). Patients who marked questions (21.7%) were older than patients without inquiries (median 63.9 vs 59.5 years; p < 0.001). The most prevalent RF was thyroid disease (23.8%). RF were considered critical for contrast-agent injection in 13.7%, requiring personalized preparation. The detection rate for RF documented with D-IPC was higher than for C-PR (n = 385 vs. 43). GHG emissions for tablet production are 80–90 times higher than for paper production. The estimated costs were slightly higher for D-IPC (+ 8.7%). Conclusion: D-IPC is feasible, but patient age is a relevant factor. Marked questions and RF help personalize IPC. The availability of patient history by D-IPC was superior compared to C-PR. [ABSTRACT FROM AUTHOR]

Post-mortem computed tomography (PMCT) has been increasingly used as routine examination in forensic pathology. No recent review of the growing number of papers on the ability of PMCT to detect skull fracture exists, and original papers report sensitivities from 0.85 to 1.00. This systematic review (PROSPERO: CRD42021233264) aims to provide a meta-analysis of sensitivity and specificity of PMCT in skull fracture detection. We searched PubMed, MEDLINE and Embase for papers published between January 2000 and August 2021 reporting raw numbers, sensitivity and specificity or Abbreviated Injury Score for PMCT compared to autopsy. Papers without both PMCT and autopsy, no separate reporting of the neuro-cranium, exclusively on children, sharp trauma, gunshot or natural death as well as case reports and reviews were excluded. Two authors independently performed inclusion, bias assessment and data extraction. QUADAS-2 was used for bias assessment and a random effects models used for meta-analysis. From 4.284 hits, 18 studies were eligible and 13 included in the meta-analysis for a total of 1538 cases. All deceased were scanned on multi-slice scanners with comparable parameters. Images were evaluated by radiologists or pathologists. Intra- and inter-observer analyses were rarely reported. In summary, sensitivity of PMCT for detection of fractures in the skull base was 0.87 [0.80; 0.92] with specificity 0.96 [0.90; 0.98], and sensitivity for the vault was 0.89 [0.80; 0.94] with specificity 0.96 [0.91; 0.98]. The mixed samples are a limitation of the review. [ABSTRACT FROM AUTHOR]

Purpose: In clinical applications, accurate histologic subtype classification of lung cancer is important for determining appropriate treatment plans. The purpose of this paper is to evaluate the role of multi-task learning in the classification of adenocarcinoma and squamous cell carcinoma. Material and methods: In this paper, we propose a novel multi-task learning model for histologic subtype classification of non-small cell lung cancer based on computed tomography (CT) images. The model consists of a histologic subtype classification branch and a staging branch, which share a part of the feature extraction layers and are simultaneously trained. By optimizing on the two tasks simultaneously, our model could achieve high accuracy in histologic subtype classification of non-small cell lung cancer without relying on physician's precise labeling of tumor areas. In this study, 402 cases from The Cancer Imaging Archive (TCIA) were used in total, and they were split into training set (n = 258), internal test set (n = 66) and external test set (n = 78). Results: Compared with the radiomics method and single-task networks, our multi-task model could reach an AUC of 0.843 and 0.732 on internal and external test set, respectively. In addition, multi-task network can achieve higher accuracy and specificity than single-task network. Conclusion: Compared with the radiomics methods and single-task networks, our multi-task learning model could improve the accuracy of histologic subtype classification of non-small cell lung cancer by sharing network layers, which no longer relies on the physician's precise labeling of lesion regions and could further reduce the manual workload of physicians. [ABSTRACT FROM AUTHOR]

To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model. [ABSTRACT FROM AUTHOR]

What are Cy Twombly's sculptures made of? This article presents an overview of a non-destructive examination conducted on three sculptures by American artist Cy Twombly (1928–2011) as part of an art-technological research project at the Doerner Institut in Munich. The artworks are part of the collection of the Brandhorst Museum and belong to Twombly's series of so-called 'Original Sculptures': assemblages of individual found objects, which the artist covered and modified with layers of plaster and white paint. To develop a long-term preservation strategy, the research focused on understanding the materials and construction methods used in Twombly's sculptures. In collaboration with the Chair of Non-Destructive Testing at the Technical University of Munich, the artworks were inspected using X-ray radiography and computed tomography. The results showed that Cy Twombly used various everyday objects made from wood, plastics, metal, and paper/cardboard to build the assemblages. Unexpectedly, the examinations revealed that the individual parts are solely held together by the coating of plaster and paint, lacking additional mechanical connections. The overall structure thus proved to be very fragile and highly sensitive to physical stresses, whether due to handling, transport, or strains in the microstructure caused by climatic fluctuations. Since little was known about Cy Twombly´s choice of materials and manufacturing details, the results offer valuable insights into the overall artistic process and decision-making of one of the most influential artists of the 20th/twenty-first centuries. Conservators can use the art-technological findings to monitor the sculptures 'condition and develop or adapt long-term preservation strategies, including aspects such as ambient climatic conditions and handling storage and transport specifications. In addition, the knowledge generated can be used for further research on the specific materials and transferred to other artworks by Cy Twombly. [ABSTRACT FROM AUTHOR]

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts. [ABSTRACT FROM AUTHOR]

This paper presents a scientific approach to catheter intervention path planning. Firstly, it gives a three-dimensional reconstruction of bronchi using Mimics software based on CT image data. It also measures and analyzes geometric morphological data of the 3D model, which includes diameter, cross-sectional area, node spacing, and curvature at different locations. Secondly, starting from the bending structure of the catheter, Adams simulation experiments and with and without constraints of the serpentine bending structure were conducted. Comparison and analysis of the experimental results of the above three experiments to obtain the quantitative relationship of the bending angle. This study established a simple control device for interventional examination, verified the effect of the method on linear motion of the device, and provided some theoretical basis for future research to realize an automatic machine for human tracheal intervention. [ABSTRACT FROM AUTHOR]

Purpose: Current research on the aging of bony orbit is usually done manually, which is inefficient and has a large error. In this paper, automatic segmentation of bony orbit based on deep learning and automatic calculation of the parameters of the segmented orbital contour (area and height of bony orbit) are presented. Methods: The craniofacial CT scanning data of 595 Chinese were used to carry out three-dimensional reconstruction and output the craniofacial images. The orbital contour images are obtained automatically by UNet++ segmentation network, and then the bony orbital area and height were calculated automatically by connected component analysis. Results: The automatic segmentation method has an Intersection of Union of 95.41% in craniofacial CT images. During the aging, the bony orbital area of males increased with age, while that of females decreased, and the area in male was larger than that in female (P < 0.05). The distance from equal points 10 and 40–90 to the supraorbital rim was significantly larger (P < 0.05). Except for the equal point 90, the distance from equal points to the inferior orbital rim was obviously larger (P < 0.05). In the females, the distance from equal points 50–70 to inferior orbital rim was significantly lower (P < 0.05). Conclusion: The method proposed here can automatically and accurately study image dataset of large-scale bony orbital CT imaging. UNet++ can achieve high-precision segmentation of bony orbital contours. The bony orbital area of Chinese changes with aging, and the bony orbital height changes different between males and females, which may be caused by the different position and degree of orbital bone resorption of males and females in the process of aging. [ABSTRACT FROM AUTHOR]

When the object contains metals, its x-ray computed tomography (CT) images are normally affected by streaking artifacts. These artifacts are mainly caused by the x-ray beam hardening effects, which deviate the measurements from their true values. One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values. Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image. We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result. This paper proposes an iterative algorithm to optimize this objective function, and the unknowns are the metal affected projections. Once the metal affected projections are estimated, the filtered backprojection algorithm is used to reconstruct the final image. This paper applies the proposed algorithm to some airport bag CT scans. The bags all contain unknown metallic objects. The metal artifacts are effectively reduced by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Core samples analysed through CT scans provide crucial reservoir information for reservoir development, encompassing porosity, permeability, pore size, grain distribution, grain sphericity and roundness, and distribution of their mineralogical assemblages. While 3D CT scans offer detailed insights, their availability is limited. This paper introduces a novel machine learning-based workflow for extracting reservoir properties from 2D CT scan images. The k-means clustering algorithm was applied to segregate and separate the constituent assemblages of the samples and consequently estimating effective porosity, clay volume, and individual pore area of the core samples. Subsequently, we employ a Monte-Carlo simulation on the Kozeny-Carman equation to predict low, mid, and high-case permeabilities of the core samples. The sphericity and roundness of the grains were estimated from the grain assemblage. Furthermore, grain sphericity and roundness were analysed using spectral clustering, another machine learning based algorithm in order to cluster the grains based on these attributes, which help in understanding the depositional transport mechanism of the grains. The feasibility of the proposed method was tested over the sandstone samples from the Lower Fulmar formation (Jurassic) and Leman formation (Permian) of the Anglo-Dutch and Central Graben basins in the North Sea. The results from the study show good correlation and inline with experimental results performed on the core samples. The methodology demonstrated is unique and has novel applications including simulating fluid flow behaviour in porous and permeable media of different constituent assemblages. [ABSTRACT FROM AUTHOR]

This paper explores the application of Visual Question-Answering (VQA) technology, which combines computer vision and natural language processing (NLP), in the medical domain, specifically for analyzing radiology scans. VQA can facilitate medical decision-making and improve patient outcomes by accurately interpreting medical imaging, which requires specialized expertise and time. The paper proposes developing an advanced VQA system for medical datasets using the Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (BLIP) architecture from Salesforce, leveraging deep learning and transfer learning techniques to handle the unique challenges of medical/radiology images. The paper discusses the underlying concepts, methodologies, and results of applying the BLIP architecture and fine-tuning approaches for VQA in the medical domain, highlighting their effectiveness in addressing the complexities of VQA tasks for radiology scans. Inspired by the BLIP architecture from Salesforce, we propose a novel multi-modal fusion approach for medical VQA and evaluating its promising potential. [ABSTRACT FROM AUTHOR]

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance. [ABSTRACT FROM AUTHOR]

Objectives: To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work. Methods: The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test. Findings: Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified. Interpretation: A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools. [ABSTRACT FROM AUTHOR]

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper explores the use of chest CT scans for early detection of COVID-19 and improved patient outcomes. The proposed method employs advanced techniques, including binary cross-entropy, transfer learning, and deep convolutional neural networks, to achieve accurate results. The COVIDx dataset, which contains 104,009 chest CT images from 1,489 patients, is used for a comprehensive analysis of the virus. A sample of 13,413 images from this dataset is categorised into two groups: 7,395 CT scans of individuals with confirmed COVID-19 and 6,018 images of normal cases. The study presents pre-trained transfer learning models such as ResNet (50), VGG (19), VGG (16), and Inception V3 to enhance the DCNN for classifying the input CT images. The binary cross-entropy metric is used to compare COVID-19 cases with normal cases based on predicted probabilities for each class. Stochastic Gradient Descent and Adam optimizers are employed to address overfitting issues. The study shows that the proposed pre-trained transfer learning models achieve accuracies of 99.07%, 98.70%, 98.55%, and 96.23%, respectively, in the validation set using the Adam optimizer. Therefore, the proposed work demonstrates the effectiveness of pre-trained transfer learning models in enhancing the accuracy of DCNNs for image classification. Furthermore, this paper provides valuable insights for the development of more accurate and efficient diagnostic tools for COVID-19. [ABSTRACT FROM AUTHOR]

Purpose: Patients with fever and inflammation of unknown origin (FUO/IUO) are clinically challenging due to variable clinical presentations with nonspecific symptoms and many differential diagnoses. Positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is increasingly used in FUO and IUO, but the optimal diagnostic strategy remains controversial. This consensus document aims to assist clinicians and nuclear medicine specialists in the appropriate use of [18F]FDG-PET/CT in FUO and IUO based on current evidence. Methods: A working group created by the EANM infection and inflammation committee performed a systematic literature search based on PICOs with "patients with FUO/IUO" as population, "[18F]FDG-PET/CT" as intervention, and several outcomes including pre-scan characteristics, scan protocol, diagnostic yield, impact on management, prognosis, and cost-effectiveness. Results: We included 68 articles published from 2001 to 2023: 9 systematic reviews, 49 original papers on general adult populations, and 10 original papers on specific populations. All papers were analysed and included in the evidence-based recommendations. Conclusion: FUO and IUO remains a clinical challenge and [18F]FDG PET/CT has a definite role in the diagnostic pathway with an overall diagnostic yield or helpfulness in 50–60% of patients. A positive scan is often contributory by directly guiding treatment or subsequent diagnostic procedure. However, a negative scan may be equally important by excluding focal disease and predicting a favorable prognosis. Similar results are obtained in specific populations such as ICU-patients, children and HIV-patients. [ABSTRACT FROM AUTHOR]

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedious and time consuming task when done manually, and expert segmentations are subject to intra- and inter-operator variability. Deep learning methods are increasingly employed to automatically carry out image segmentation tasks. These networks usually need to be trained on a large image dataset along with the manual segmentations to maximize generalization to new images, but it is not always possible to have access to a multitude of CT-scans with the associated ground truth. It then becomes necessary to use training techniques to make the best use of the limited available data. In this paper, we propose a dedicated pipeline of preprocessing, deep learning based segmentation method and post-processing for in-vivo human femurs and vertebrae segmentation from CT-scans volumes. We experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation if carefully trained. We compared the failure load simulation results obtained on femurs and vertebrae using either automatic or manual segmentations and studied the sensitivity of the simulations on small variations of the automatic segmentation. The failure loads obtained using automatic segmentations were comparable to those obtained using manual expert segmentations for all the femurs and vertebrae tested, demonstrating the effectiveness of the automated segmentation approach for failure load simulations. [ABSTRACT FROM AUTHOR]