Search

Aims. The main objective of this study was to determine the sensitivity of abdominal ultrasonography (US) in patients with isoattenuating pancreatic carcinoma and to compare the frequency of secondary signs on abdominal US and endoscopic ultrasonography (EUS) in these tumours. Methods. Twenty-four patients with histologically or cytologically verified isoattenuating pancreatic carcinoma who underwent abdominal US, contrast-enhanced CT and EUS of the pancreas as part of the diagnostic workup were included in this retrospective study. The sensitivity of abdominal US in detecting the isoattenuating pancreatic carcinoma was investigated and the frequency of secondary signs of isoattenuating pancreatic carcinoma on abdominal US and EUS was compared. Results. In 5 of 24 patients (21%) with isoattenuating pancreatic carcinoma, a hypoechogenic pancreatic lesion was directly visualised on abdominal US. Secondary signs were present on US in 21 patients (88%). These included dilatation of the common bile duct and/or intrahepatic bile ducts in 19/24 (79%), dilatation of the pancreatic duct in 3/24 (13%), abnormal contour/inhomogeneity of the pancreas in 1/24 (4%), and atrophy of the distal parenchyma in 1/24 (4%). Pancreatic duct dilatation was observed more frequently on EUS than on abdominal US (P=0.002). For other secondary signs, there was no significant difference in their detection on abdominal US and EUS (P=0.61-1.00). Conclusion. Abdominal US is capable of detecting secondary signs of isoattenuating pancreatic carcinoma with high sensitivity and has the potential to directly visualise these tumours.

The advent of various new-age radiological modalities over the years has brought about paradigm shifts in diagnostics for clinicians all over the world. These advancements have refined medical practice immeasurably. Accurate diagnosis have, in turn, enabled surgeons to administer appropriate and timely surgical therapy to their patients. The timeline of path-breaking radiological inventions from X-rays to Ultrasonics, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and beyond, bears living testimony to the role that preoperative imaging plays in a surgeon’s daily life. The present review paper nostalgically remembers and acknowledges the pioneers of this remarkable journey. Additionally, it shares the authors’ experiences with classical radiological signs and appearances correlated with their corresponding surgical ‘first look’ pictures from some of the rare and interesting cases the authors have encountered over the years. The present cases exhibit diverse presentations, clinical signs, radiological findings and diagnosis. The crucial role played by radiology in clinicians’ daily lives, particularly in accurately diagnosing rare conditions, underscores the need to report these diverse cases.

Abstract Background Mild traumatic brain injury (mTBI), i.e. a TBI with an admission Glasgow Coma Scale (GCS) of 13–15, is a common cause of emergency department visits. Only a small fraction of these patients will develop a traumatic intracranial hemorrhage (tICH) with an even smaller subgroup suffering from severe outcomes. Limitations in existing management guidelines lead to overuse of computed tomography (CT) for emergency department (ED) diagnosis of tICH which may result in patient harm and higher healthcare costs. Objective To perform a systematic review and meta-analysis to characterize known and potential novel risk factors that impact the risk of tICH in patients with mTBI to provide a foundation for improving existing ED guidelines. Methods The literature was searched using MEDLINE, EMBASE and Web of Science databases. Reference lists of major literature was cross-checked. The outcome variable was tICH on CT. Odds ratios (OR) were pooled for independent risk factors. Results After completion of screening, 17 papers were selected for inclusion, with a pooled patient population of 26,040 where 2,054 cases of tICH were verified through CT (7.9%). Signs of a skull base fracture (OR 11.71, 95% CI 5.51–24.86), GCS

Purpose: Solutions of iodine-based compounds, due to their high X-ray attenuation coefficient, are widely used as contrast agents in computed tomography (CT) imaging. This paper investigates the attenuation properties of iodine and gold to develop nanoparticle-based contrast agents, for example, composite nanoparticles (NPs) with layers of iodine and gold or a mixture of NPs of gold and iodine. Materials and Methods: A theoretical formula is derived that gives the Hounsfield Unit (HU) for different weight-by-weight (w/w) concentrations of a mixture of blood + iodine + gold. The range of compositions for which iodine + gold mixture can give a suitable HU ≥250 upon being mixed with blood, is formulated. These estimates are derived from experiments on the variation of HU values in different compositions of aqueous solutions of iodine and available data for gold. Results: It is seen that for an aqueous solution of iodine, the suitable HU of 250 (hence giving sufficient gray level to the CT image) can be obtained with w/w concentrations of iodine being 0.0044, 0.008, and 0.0097 for observations at 80, 100, and 120 kVp, respectively. The corresponding w/w concentrations of gold NPs would be 0.0103, 0.0131, and 0.0158. With these basic results, compositions of suitable mixtures of iodine and gold are also specified. Conclusion: Aqueous suspensions of gold NPs are suitable as contrast materials for CT imaging and can also be used as a component of a composite contrast material consisting of an iodine and gold mixture.

Ovarian lymphangiomas are rare benign neoplasms characterized by the proliferation of lymphatic vessels within the ovarian tissue. While lymphangiomas can manifest in various anatomical locations, their occurrence within the ovaries is exceptionally uncommon, posing diagnostic and therapeutic challenges for clinicians. The aetiology of ovarian lymphangiomas remains elusive, with theories suggesting congenital malformations, lymphatic obstruction, or acquired lymphatic proliferation as potential contributing factors. The clinical presentation of ovarian lymphangiomas often includes nonspecific symptoms such as abdominal pain, swelling, or discomfort, leading to difficulties in early detection and diagnosis. Radiological imaging, particularly Ultrasound, CT (computed tomography) and MRI (magnetic resonance imaging), plays a crucial role in identifying these lesions and guiding subsequent management strategies. Despite their generally benign nature, ovarian lymphangiomas can attain significant sizes, causing complications such as torsion, rupture, or compression of adjacent structures. Surgical intervention, typically in cystectomy or oophorectomy, is frequently pursued to alleviate symptoms and prevent potential complications. This paper aims to comprehensively review the existing literature on ovarian lymphangiomas, addressing their clinical presentation, diagnostic challenges, and management strategies. By synthesizing available data, we seek to enhance our understanding of this rare entity, providing valuable insights for clinicians encountering similar cases. Improved awareness and knowledge of ovarian lymphangiomas are essential for timely diagnosis and optimal patient outcomes.

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

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.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

BackgroundThe success of using bone mineral density and/or FRAX to predict femoral osteoporotic fracture risk is modest since they do not account for mechanical determinants that affect bone fracture risk. Computed Tomography (CT)-based geometric, densitometric, and finite element-derived biomarkers have been developed and used as parameters for assessing fracture risk. However, to quantify these biomarkers, segmentation of CT data is needed. Doing this manually or semi-automatically is labor-intensive, preventing the adoption of these biomarkers into clinical practice. In recent years, fully automated methods for segmenting CT data have started to emerge. Quantifying the accuracy, robustness, reproducibility, and repeatability of these segmentation tools is of major importance for research and the potential translation of CT-based biomarkers into clinical practice.MethodsA comprehensive literature search was performed in PubMed up to the end of July 2024. Only segmentation methods that were quantitatively validated on human femurs and/or pelvises and on both clinical and non-clinical CT were included. The accuracy, robustness, reproducibility, and repeatability of these segmentation methods were investigated, reporting quantitatively the metrics used to evaluate these aspects of segmentation. The studies included were evaluated for the risk of, and sources of bias, that may affect the results reported.FindingsA total of 54 studies fulfilled the inclusion criteria. The analysis of the included papers showed that automatic segmentation methods led to accurate results, however, there may exist a need to standardize reporting of accuracy across studies. Few works investigated robustness to allow for detailed conclusions on this aspect. Finally, it seems that the bone segmentation field has only addressed the concept of reproducibility and repeatability to a very limited extent, which entails that most of the studies are at high risk of bias.InterpretationBased on the studies analyzed, some recommendations for future studies are made for advancing the development of a standardized segmentation protocol. Moreover, standardized metrics are proposed to evaluate accuracy, robustness, reproducibility, and repeatability of segmentation methods, to ease comparison between different approaches.

Primary undifferentiated high-grade pleomorphic sarcoma (UPS) in the mediastinum is exceptionally rare. This paper reports a unique case of anterior mediastinal UPS in an 84-year-old Asian male presenting with recurrent upper respiratory tract infections and upper abdominal discomfort. Imaging via CT and MRI suggested an invasive thymoma, but postoperative pathology confirmed UPS. Despite radical surgery, local recurrence occurred within three months, and palliative radiotherapy was ineffective. This case provides the first comprehensive imaging data of UPS in the anterior mediastinum, aiming to improve diagnostic accuracy for clinicians and radiologists by summarizing imaging features across various modalities.

Background and purpose:: In radiotherapy, precise comparison of fan-beam computed tomography (CT) and cone-beam CT (CBCT) arises as a commonplace, yet intricate task. This paper proposes a publicly available end-to-end pipeline featuring an intrinsic deep-learning-based speedup technique for generating virtual 3D and 4D CBCT from CT images. Materials and methods:: Physical properties, derived from CT intensity information, are obtained through automated whole-body segmentation of organs and tissues. Subsequently, Monte Carlo (MC) simulations generate CBCT X-ray projections for a full circular arc around the patient employing acquisition settings matched with a clinical CBCT scanner (modeled according to Varian TrueBeam specifications). In addition to 3D CBCT reconstruction, a 4D CBCT can be simulated with a fully time-resolved MC simulation by incorporating respiratory correspondence modeling. To address the computational complexity of MC simulations, a deep-learning-based speedup technique is developed and integrated that uses projection data simulated with a reduced number of photon histories to predict a projection that matches the image characteristics and signal-to-noise ratio of the reference simulation. Results:: MC simulations with default parameter setting yield CBCT images with high agreement to ground truth data acquired by a clinical CBCT scanner. Furthermore, the proposed speedup technique achieves up to 20-fold speedup while preserving image features and resolution compared to the reference simulation. Conclusion:: The presented MC pipeline and speedup approach provide an openly accessible end-to-end framework for researchers and clinicians to investigate limitations of image-guided radiation therapy workflows built on both (4D) CT and CBCT images.

Introduction: Foreign body aspiration (FBA) is a common, life-threatening pediatric emergency and was shown to be associated with high risk of morbidity and mortality. This systematic review and meta-analysis aimed to investigate the diagnostic value of chest computed tomography (CT) scan for identification of FBA in children. Methods: From inception to May 2024, a systematic search was carried out across multiple databases including Medline, Scopus, and Web of Science, considering published papers in English language. Quality assessment of the included studies was performed using seven domains of Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results: The systematic literature search yielded 7203 articles. The pooled sensitivity and specificity of chest CT scan for identification of FBA were 0.99 (95% CI: 0.98-0.99) and 0.97 (95% CI: 0.96-0.98), respectively. The pooled positive likelihood ratio was 10.12 (95% CI: 4.59-22.20), and pooled negative likelihood ratio was 0.05 (95% CI: 0.02-0.1). Furthermore, the area under the summarized receiver operating characteristic (SROC) curve was 0.98. Conclusion: Our meta-analysis revealed that despite high heterogeneity, in the diagnostic characteristics of chest CT scan among studies, it has high diagnostic value in identifying FBA in suspected pediatric cases.

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.

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 Background This paper attempts to conduct a systematic review and meta-analysis of deep learning (DLs) models for cervical cancer CT image segmentation. Methods Relevant studies were systematically searched in PubMed, Embase, The Cochrane Library, and Web of science. The literature on DLs for cervical cancer CT image segmentation were included, a meta-analysis was performed on the dice similarity coefficient (DSC) of the segmentation results of the included DLs models. We also did subgroup analyses according to the size of the sample, type of segmentation (i.e., two dimensions and three dimensions), and three organs at risk (i.e., bladder, rectum, and femur). This study was registered in PROSPERO prior to initiation (CRD42022307071). Results A total of 1893 articles were retrieved and 14 articles were included in the meta-analysis. The pooled effect of DSC score of clinical target volume (CTV), bladder, rectum, femoral head were 0.86(95%CI 0.84 to 0.87), 0.91(95%CI 0.89 to 0.93), 0.83(95%CI 0.79 to 0.88), and 0.92(95%CI 0.91to 0.94), respectively. For the performance of segmented CTV by two dimensions (2D) and three dimensions (3D) model, the DSC score value for 2D model was 0.87 (95%CI 0.85 to 0.90), while the DSC score for 3D model was 0.85 (95%CI 0.82 to 0.87). As for the effect of the capacity of sample on segmentation performance, no matter whether the sample size is divided into two groups: greater than 100 and less than 100, or greater than 150 and less than 150, the results show no difference (P > 0.05). Four papers reported the time for segmentation from 15 s to 2 min. Conclusion DLs have good accuracy in automatic segmentation of CT images of cervical cancer with a less time consuming and have good prospects for future radiotherapy applications, but still need public high-quality databases and large-scale research verification.

In current review, we consider the Russian and, mainly, international experience of the “digital core» technology, namely – the possibility of creating a numerical models of internal structure of the cores and multiphase flow at pore space scale. Moreover, our paper try to gives an answer on a key question for the industry: if digital core technology really allows effective to solve the problems of the oil and gas field, then why does it still not do this despite the abundance of scientific work in this area? In particular, the analysis presented in the review allows us to clarify the generally skeptical attitude to technology, as well as errors in R&D work that led to such an opinion within the oil and gas companies. In conclusion, we give a brief assessment of the development of technology in the near future.

Sparse-view computed tomography (CT) is an important way to reduce the negative effect of radiation exposure in medical imaging by skipping some X-ray projections. However, due to violating the Nyquist/Shannon sampling criterion, there are severe streaking artifacts in the reconstructed CT images that could mislead diagnosis. Noting the ill-posedness nature of the corresponding inverse problem in a sparse-view CT, minimizing an energy functional composed by an image fidelity term together with properly chosen regularization terms is widely used to reconstruct a medical meaningful attenuation image. In this paper, we propose a regularization, called the box-constrained nonlinear weighted anisotropic total variation (box-constrained NWATV), and minimize the regularization term accompanying the least square fitting using an alternative direction method of multipliers (ADMM) type method. The proposed method is validated through the Shepp-Logan phantom model, alongisde the actual walnut X-ray projections provided by Finnish Inverse Problems Society and the human lung images. The experimental results show that the reconstruction speed of the proposed method is significantly accelerated compared to the existing $ L_1/L_2 $ regularization method. Precisely, the central processing unit (CPU) time is reduced more than 8 times.

Bronchiectasis is a chronic disease syndrome manifested by a productive cough, subsequent to structural changes in the bronchial walls resulting from bacterial infection and leading to the retention of secretions. In some patients, the cause of bronchiectasis is chronic diseases, such as cystic fibrosis, ciliary dyskinesia, α1-antitrypsin deficiency, or congenital immune disorders. However, there are cases of bronchiectasis without underlying chronic disease, e.g. as a consequence of severe lower respiratory tract infection (bacterial or viral) or local airway obstruction, caused by foreign body aspiration or external pressure. Currently, it is believed that in some patients bronchial deformations may be reversible with a sufficiently quick diagnosis and implementation of treatment. The paper presents the cases of 3 children in whom the resolution of dilated lesions was confirmed by computed tomography.

Congenital lung malformations are a constellation of pathologies that can be diagnosed antenatally by ultrasound and fetal MRI. Ultrasound is considered the modality of choice for a routine assessment of second-trimester scans worldwide. Bronchopulmonary sequestration (BPS) and congenital pulmonary airway malformation (CPAM) are the 2 most common echogenic chest masses discovered incidentally during routine ultrasound scans in the second trimester. This paper describes BPS and differentiates it from CPAM sonographically in utero. An extensive literature search involving antenatal ultrasound is undertaken to review the most up-to-date understanding of the BPS. Furthermore, a case study at our institution and the literature review will help better describe the salient features of BPS. A 41-year-old female G3P1 visits our department for a routine second-trimester ultrasound. An echogenic lesion with a cystic component is visualized in this scan. Based on the grayscale and color imaging, this complex echogenic lesion was reported as CPAM and was referred to fetal assessment for confirmation. The fetal assessment diagnosed the lesion as BPS because of the pathognomonic feeding vessel from the thoracic aorta. Regardless of the congenital lung mass, any large mass compromising fetal well-being is an indication for intervention. The prognosis of BPS in the absence of fetal hydrops is excellent. A robust collaboration among radiologists, obstetricians, and pediatricians is required for the best outcome for the pregnancy and the neonate.

Atrial fibrillation (AF) is the most common supraventricular arrhythmia. Despite significant advances in its treatment, it still remains one of the leading causes of cardiovascular morbidity and mortality. In the last two decades, pulmonary vein isolation (PVI) was developed as the most effective treatment option. The reported effectiveness of a single ablation procedure ranges from 40% to 69% with single, and up to 88% with repeated procedures, with acceptable safety profile. The PubMed database was searched, using terms including ‘atrial fibrillation ablation’, ‘pulmonary vein isolation’, ‘computed tomography’, ‘pulmonary vein anatomy’ and ‘ovality index’. Papers were reviewed for relevance and scientific merit. Different imaging techniques are used for pre-procedural assessment of left atrial (LA) anatomy, of which computed tomography (CT) is the most common. It allows assessing pulmonary vein (PV) anatomy, the LA wall thickness in different regions and the left atrial appendage (LAA) anatomy, together with excluding the presence of intracardiac thrombi. Pre-procedural PVs imaging is important regardless of the selected ablation technique, however, cryoballoon (CB) ablation seems to be particularly anatomy-dependent. Additionally, CT also permits assessment of several PVs characteristics (geometry, dimensions, angulations, the ostium area, orientation and ovality index (OI), which are essential for the patients’ qualification and designing the strategy of AF ablation. In this paper, we have reviewed the role of CT imaging in patients undergoing ablation procedure due to recurrent/symptomatic atrial fibrillation. Moreover, we discussed the relevant literature.

A 74-year-old woman underwent robot-assisted left partial nephrectomy for left renal cell carcinoma (RCC) at another hospital 5 years ago. However, the tumor recurred, and transarterial embolization (TAE) and radiofrequency ablation were planned. At finding of recurrence tumor, computed tomography angiography (CTA) also showed a left renal artery aneurysm (RAA). However, it was actually a disseminated tumor of RCC fed by the capsular artery, which was diagnosed by catheter-based angiography. Combined segmental artery resection was safely performed under robotic assistance. The pathological diagnosis was a recurrence of RCC. No continuity with the renal artery wall was observed, and vascular invasion was not evident. CTA is very useful in diagnosing RAA. However, CTA alone may lead to misdiagnosis. This paper reveals the pitfalls when diagnosing RAA with CTA. For accurate diagnosis, a combination of CTA and catheter-based angiography should be used.

Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model’s metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score.

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.

The paper presents a brief analysis of publications utilizing the public SARS-CoV-2 dataset, consisting of patients’ computer tomography scans captured from Brazil hospitals and an experimental setup addressing the found data challenges. The analysis shows that all protocols, with one exception, suffer from data leakage arising from data organization where the patients and their images are not grouped. Each patient is represented with several scans. It can provide misleading results as data of the same individual may occur in both training and test sets. Furthermore, only one paper proposed ensemble learning utilizing as base models VGG-16, ResNet50, and Xception. Therefore, we proposed and experimented with the following strategy to mitigate the found risks of bias: data standardization and normalization to achieve proper contrast and resolution; k-means and group shuffle split to avoid data leakage; augmentation and ensemble transfer learning to deal with limited sample size and over-fitting. Compared with the earlier proposed ensemble approach, the current one stacks VGG-16, Densenet-201, and Inception v3, achieving higher accuracy (99.3 %), second in the related work, and most significantly, it applies augmentation and clustering analysis to avoid overestimation. In contrast, the paper also presented critical metrics in the medical domain: negative prediction value (99.55%), false positive rate (0.89%), false negative rate (0.42%), and false discovery rate (0.83%). The strategy has two main advantages: reducing data pitfalls and decreasing generalization error. It can serve as a baseline to increase the performance quality and mitigate the risk of bias in the field.

Abstract Knowing human papillomavirus (HPV) status has important consequences for treatment selection in oropharyngeal cancer. The gold standard is to perform a biopsy. The objective of this paper is to develop a new computed tomography (CT) radiomics-based non-invasive solution to HPV status determination and investigate if and how it can be a viable and accurate complementary technique. Two hundred thirty-eight patients’ CT scans were normalized and resampled. One thousand one hundred forty-two radiomics features were obtained from the segmented CT scans. The number of radiomic attributes was decreased by applying correlation coefficient analysis, backward elimination, and random forest feature importance analysis. Random over-sampling (ROSE) resampling algorithm was performed on the training set for data balancing, and as a result, 161 samples were obtained for each of the HPV classes of the training set. A random forest (RF) classification algorithm was used as a prediction model using five-fold cross-validation (CV). Model effectiveness was evaluated on the unused 20% of the imbalanced data. The applicability of the model was investigated based on previous research and error rates reported for biopsy procedures. The HPV status was determined with an accuracy of 91% (95% CI 83–99) and an area under the curve (AUC) of 0.77 (95% CI 65–89) on the test data. The error rates were comparable to those encountered in biopsy. As a conclusion, radiomics has the potential to predict HPV status with accuracy levels that are comparable to biopsy. Future work is needed to improve standardization, interpretability, robustness, and reproducibility before clinical translation.

Automatic segmentation of vertebrae from computed tomography (CT) scans play an important role in the clinical interpretation and treatment of spinal co-morbidities. Labelling and segmentation of vertebrae is labour intensive and challenging, due to various fields of view and fuzzy boundaries in CT scans. Therefore, successful labelling and segmentation is highly dependent on the level of expertise of the radiologist. In this paper, we propose a three-step fully-automated end-to-end pipeline for vertebra labelling and segmentation of spinal CT images. A novel deep learning architecture, Unbalanced-UNet, is proposed for extracting the region proposals for spine detection. A modified SpatialConfiguration-Net, 3D SCN, is used for labelling of vertebra and centroid extraction. Finally, a 3D U-Net is employed for the segmentation of each vertebra. The models were validated on VERSE’19 public dataset. An identification rate of 90.20% and 91.47% was obtained for the first and second test sets of the VERSE’19 dataset, respectively. Mean localization distance of 4.97 mm and 5.32 mm was obtained for the first and second test sets, respectively. The final segmentation stage shows a dice score and Hausdorff surface distance of 93.07% and 5.36 mm, respectively, for the first test set, and 92.01% and 5.63 mm, respectively, for the second test set. The results show that the proposed approach outperforms the state-of-art models for segmentation of vertebrae. The proposed Unbalanced-UNet architecture increased the accuracy of accruing the region proposals for spine detection. The proposed fully automated pipeline has potential clinical applications in treatment and surgical planning of spinal deformities.

Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

A fast and precise diagnosis is crucial for the treatment and management of lung diseases, which are a major global cause of morbidity and mortality. Medical diagnosis and treatment planning depend heavily on the classification of lung diseases. The correct diagnosis and classification of many lung disease types is crucial for effective management and treatment. Radiologists with training evaluate medical images subjectively in order to classify lung diseases using traditional approaches. This paper proposed an effective technique for classifying lung diseases from CXR images. For the accurate classification of lung disorders, three distinct fine-tuned models are proposed. The effectiveness of the suggested fine-tuned models was evaluated using a newly developed CXR image dataset. According to the experimental findings, the proposed fine-tuned models outperformed the existing lung disease categorization models the accuracy is 98%. The suggested approach can effectively be used for lung disease classification.

Brain computed tomography (CT) is a diagnostic imaging tool routinely used to assess all paediatric neurologic disorders and other head injuries. Despite the continuous development of paediatric CT imaging, radiation exposure remains a concern. Using diagnostic reference levels (DRLs) helps to manage the radiation dose delivered to patients, allowing one to identify an unusually high dose. In this paper, we propose DRLs for paediatric brain CT examinations in Saudi clinical practices and compare the findings with those of other reported DRL studies. Data including patient and scanning protocols were collected retrospectively from three medical cities for a total of 225 paediatric patients. DRLs were derived for four different age groupings. The resulting DRL values for the dose–length product (DLP) for the age groups of newborns (0–1 year), 1-y-old (1–5 years), 5-y-old (5–10 years) and 10-y-old (10–15 years) were 404 mGy cm, 560 mGy cm, 548 mGy cm, and 742 mGy cm, respectively. The DRLs for paediatric brain CT imaging are comparable to or slightly lower than other DRLs due to the current use of dose optimisation strategies. This study emphasises the need for an international standardisation for the use of weight group categories in DRL establishment for paediatric care in order to provide a more comparable measurement of dose quantities across different hospitals globally.

Abstract 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.

The SAXSMAT beamline P62 (Small-Angle X-ray Scattering beamline for Materials Research) is a new beamline at the high-energy storage ring PETRA III at DESY. This beamline is dedicated to combined small- and wide-angle X-ray scattering (SAXS/WAXS) techniques for both soft and hard condensed matter systems. It works mainly in transmission geometry. The beamline covers an energy range from 3.5 keV to 35.0 keV, which fulfills the requirements of the user community to perform anomalous scattering experiments. Mirrors are used to reduce the intensity of higher harmonics. Furthermore, the mirrors and 2D compound refracting lenses can focus the beam down to a few micrometres at the sample position. This option with the high photon flux enables also SAXS/WAXS tensor tomography experiments to be performed at this new beamline in a relatively short time. The first SAXS/WAXS pattern was collected in August 2021, while the first user experiment was carried out two months later. Since January 2022 the beamline has been in regular user operation mode. In this paper the beamline optics and the SAXS/WAXS instrument are described and two examples are briefly shown.

Mountain red soil, as a special type of soil in the South, has received widespread attention for its soil erosion problems. Its pore structure restricts water infiltration, thereby affecting the occurrence and development of soil erosion. In order to systematically obtain the distribution characteristics of the pore structure within the surface mountain red soil, this paper uses non-destructive CT detection technology to scan the soil column samples taken from the typical mountain red soil distribution area in Chenggong District, Kunming City, Yunnan Province. Image processing technology is applied to CT slices, and ImageJ (1.46r) software is used to obtain the distribution characteristics of pores within the soil column, including pore sizes and the number of pores at each depth, the proportion of pore area, roundness, and box-counting dimension. The results show that with the increase in depth, the proportion of pore area decreases linearly from the maximum value of 52.25% at the top to the minimum value of 2.02% at the bottom; the roundness of pores fluctuates between 0.8 and 0.9, overall increasing; the total number of pores generally first increases then decreases, and small pores are predominant, with the least number of large pores in the topsoil layer; the box-counting dimension shows a gradual linear decrease, with a maximum value of 1.7980 and a minimum value of 0.9878. The number of pores affects both roundness and the box-counting dimension, and the proportion of pore area also affects the box-counting dimension. There is a negative correlation between roundness and the box-counting dimension. The 3D visualization reconstruction of pores shows that most are interconnected, with the pore size significantly reducing with increasing depth. The quantitative analysis of parameters and 3D visualization reveal, to some extent, the impact of pore structure on the occurrence and development of soil erosion in mountain red soil. These research findings form the foundation for studying soil erosion in this region and provide a basis for systematically understanding its processes and mechanisms.

Background: The development of novel medical imaging technologies and treatment procedures hinges on the availability of accurate and versatile phantoms. This paper presents a cost-effective approach for creating anthropomorphic abdominal phantoms. Methods: This study proposes a cost-effective method using 3D printing and readily available materials (beeswax, plaster, and epoxy resin) to create high-fidelity anthropomorphic abdominal phantoms. The three-dimensionally printed phantoms exhibited X-ray attenuation properties closely matching those of human tissues, with measured Hounsfield unit (HU) values of −115.41 ± 20.29 HU for fat, 65.61 ± 18.06 HU for muscle, and 510 ± 131.2 HU for bone. These values were compared against patient images and a commercially available phantom, and no statistically significant difference was observed in fat tissue simulation (p = 0.428). Differences were observed for muscle and bone tissues, in which the 3D-printed phantom demonstrated higher HU values compared with patient images (p < 0.001). The 3D-printed phantom’s bone simulation was statistically like that of the commercially available phantom (p = 0.063). Conclusion: This method offers a cost-effective, accessible, and customizable alternative for abdominal phantoms. This innovation has the potential to accelerate advancements in abdominal imaging research, leading to improved diagnostic tools and treatment options for patients. These phantoms could be used to develop and test new imaging techniques with high accuracy.

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical–radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness.

Objectives. The first objective of our study was to determine the radiation exposure received by patients during tin-filtrated ultra-low-dose computed tomography (TFULDCT) of sacroiliac joints and to compare those to conventional X-ray doses. For comparison, we added a cohort examined by low-dose CT (LDCT) without tin filtration. The second objective was to compare the results of TFULDCT and X-ray in the detection of sacroiliitis. Methods. Our retrospective study covered 45 patients, who were examined for suspected axial spondyloarthritis (AxSpA). The first group underwent TFULDCT as well as conventional radiography (CR); the second group underwent LDCT only without tin filtration. Effective doses of TFULDCT, LDCT and CR were calculated by an experienced medical physicist. TFULDCT and CR were independently evaluated by three investigators, who decided on the presence or absence of rheumatoid inflammatory bone changes. The results were statistically evaluated. Results. In our cohort, the median effective dose for TFULDCT was 0.11 mSv, range (0.06-0.40 mSv), for LDCT 0.5 mSv (0.29-0.89 mSv), and for CR 0.25 mSv (0.06-1.87 mSv). We proved that TFULDCT produces a significantly lower percentage of uncertain results (23.3%; 95% CI: 11.3-41.6%) than CR (66.7%; 95% CI: 48.3-81.1%). Conclusions. Tin filtration helps to reduce CT radiation exposure to values lower than those resulting from CR. TFULDCT offers better overall diagnostic performance than CR. Our results prove that TFULDCT can replace CR in the diagnosis of sacroiliitis in the radiographical stage of AxSpA.

Abstract Background This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC). Methods Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021. Results We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs. Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance. Conclusions Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.

Dual energy computed tomography (DECT) plays an important role in the application of advanced imaging due to its material decomposition capability. Image domain decomposition can directly invert CT images through by linear matrix, but the decomposed material images will be seriously affected by noise and artifacts. Although various regularization methods have been proposed to solve this problem, they still face two challenges: tedious parameter adjustment and the loss of image details resulted from over-smoothing. Therefore, in this paper we proposes a dual energy CT image material decomposition algorithm based on iterative residual network. Direct inversion is used as the initial base image, and a stacking two-channel convolutional neural network is used to replace the regularization items in the iterative decomposition model to form a deep iterative decomposition network. This method can realize material decomposition and noise suppression simultaneously. Experimental results show that the iterative residual network proposed in this paper is superior to other comparison methods and can effectively suppress noise and artifacts while maintaining the edge details of the base image.

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Background and objective: Pelvic bone tumors represent a harmful orthopedic condition, encompassing both benign and malignant forms. Addressing the issue of limited accuracy in current machine learning algorithms for bone tumor image segmentation, we have developed an enhanced bone tumor image segmentation algorithm. This algorithm is built upon an improved full convolutional neural network, incorporating both the fully convolutional neural network (FCNN-4s) and a conditional random field (CRF) to achieve more precise segmentation. Methodology: The enhanced fully convolutional neural network (FCNN-4s) was employed to conduct initial segmentation on preprocessed images. Following each convolutional layer, batch normalization layers were introduced to expedite network training convergence and enhance the accuracy of the trained model. Subsequently, a fully connected conditional random field (CRF) was integrated to fine-tune the segmentation results, refining the boundaries of pelvic bone tumors and achieving high-quality segmentation. Results: The experimental outcomes demonstrate a significant enhancement in segmentation accuracy and stability when compared to the conventional convolutional neural network bone tumor image segmentation algorithm. The algorithm achieves an average Dice coefficient of 93.31 %, indicating superior performance in real-time operations. Conclusion: In contrast to the conventional convolutional neural network segmentation algorithm, the algorithm presented in this paper boasts a more intricate structure, proficiently addressing issues of over-segmentation and under-segmentation in pelvic bone tumor segmentation. This segmentation model exhibits superior real-time performance, robust stability, and is capable of achieving heightened segmentation accuracy.

Elongated anisotropic Gaussian filters are used for the orientation estimation of fibers. In cases where computed tomography images are noisy, roughly resolved, and of low contrast, they are the method of choice even if being efficient only in virtual 2D slices. However, minor inaccuracies in the anisotropic Gaussian filters can carry over to the orientation estimation. Therefore, this paper proposes a modified algorithm for 2D anisotropic Gaussian filters and shows that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, the effectiveness of the approach is shown by applying it to real-world images of sheet molding compounds.

Aim: The overall goal of this review was to examine the cost-utility of robotic-arm assisted surgery versus manual surgery. Methods: We performed a systematic review of all health economic studies that compared CT-based robotic-arm assisted unicompartmental knee arthroplasty, total knee arthroplasty and total hip arthroplasty with manual techniques. The papers selected focused on various cost-utility measures. In addition, where appropriate, secondary aims encompassed various clinical outcomes (e.g., readmissions, discharges to subacute care, etc.). Only articles directly comparing CT-based robotic-arm assisted joint arthroplasty with manual joint arthroplasty were included, for a resulting total of 21 reports. Results: Almost all twenty-one studies demonstrated a positive effect of CT scan-guided robotic-assisted joint arthroplasty on health economic outcomes. For studies reporting on 90-day episodes of costs, 10 out of 12 found lower costs in the robotic-arm assisted groups. Conclusion: Robotic-arm assisted joint arthroplasty patients had shorter lengths of stay and cost savings based on their 90-day episodes of care, among other metrics. Payors would likely benefit from encouraging the use of this CT-based robotic technology.

Primary retroperitoneal tumors are a heterogeneous group of neoplasms with a prevalence of 0.02 to 1%. The results of diagnostic visualization of such tumors are usually ambiguous due to their diversity and similar radiological semiotics of malignant and benign tumor types. The paper describes a rare case of the diagnosis and management of a patient with one of retroperitoneal tumor, i. e. a giant intramuscular hemangioma. A 23-year old male patient was referred with complaints of episodic pains in the right inguinal area for 78 years. The multiaxial computed tomography showed a mass of 145 125 125 mm located in the trunk and pelvis, and spreading to the right thigh. The tumor structure was markedly heterogeneous and contained calcinates, adipose tissue, soft tissue and vascular components. The mass showed a heterogeneous contrast accumulation, with predominant delayed-phase enhancement. The tumor was surgically resected, with the histological investigation characteristic of intramuscular hemangioma. Rare retroperitoneal tumors are always challenging in the interpretation of the results of diagnostic visualization. Since their various types have similar radiological semiotics, the differential diagnosis is often impossible. Therefore, the main diagnostic issue should be the exact localization of the neoplasm, identification of the feeding vessels, and the assessment of the adjacent tissue abnormalities.

Abstract Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three‐dimensional (3D) segmentation of CT scans, opposed to single L3‐slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground‐truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole‐body CT, but it was not clear whether this truly meant head‐to‐fingertip‐to‐toe. Two studies used conventional computer algorithms. The latter five used deep learning (DL), an artificial intelligence technique where algorithms are similarly organized to brain neuronal pathways. Six of seven reported excellent segmentation performance (Dice similarity coefficients > 0.9 per tissue). Internal testing on unseen scans was performed for only four of seven algorithms, whilst only three were tested externally. Trained DL algorithms achieved full CT segmentation in 12 to 75 s versus 25 min for non‐DL techniques. DL enables opportunistic, rapid and automated volumetric BC analysis of CT performed for clinical indications. However, most CT scans do not cover head‐to‐fingertip‐to‐toe; further research must validate using common CT regions to estimate true whole‐body BC, with direct comparison to single lumbar slice. Due to successes of DL, we expect progressive numbers of algorithms to materialize in addition to the seven discussed in this paper. Researchers and clinicians in the field of BC must therefore be aware of pitfalls. High Dice similarity coefficients do not inform the degree to which BC tissues may be under‐ or overestimated and nor does it inform on algorithm precision. Consensus is needed to define accuracy and precision standards for ground‐truth labelling. Creation of a large international, multicentre common CT dataset with BC ground‐truth labels from multiple experts could be a robust solution.

This review article presents an analysis of 36 scientific papers focusing on modern three-dimensional (3D) reconstruction techniques in computed tomography (CT) and magnetic resonance imaging (MRI) for their applications in medical diagnostics. The objective of this review is to present the current state of knowledge regarding the development and utilization of 3D reconstruction techniques, as well as to identify key trends and challenges in this field. The first part of the study focuses on the advancements in MRI and CT. The analysis reveals the major trends in the evolution of these diagnostic methods, such as increased accessibility of CT and MRI examinations for patients, reduced scan duration, greater utilization of artificial intelligence, and expanded applications in interventional radiology.The second part of the article highlights the potential and effectiveness of 3D modelling in diagnostic imaging. Creating 3D models of anatomical structures is a complex and multi-step process. Through the review, it was determined that 3D models derived from MRI can be equally accurate and diagnostically valuable compared to the more commonly used CT-based reconstructions. In the future, fusion imaging of MRI/CT is expected to play an increasingly significant role in orthopaedic imaging. The review demonstrates the significant potential of 3D modelling in diagnostic imaging. However, further research is still required to better understand the capabilities of 3D modelling in diagnosing complex anatomical structures. The integration of information technology in medicine will be crucial in advancing this field.

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.

Synchrotron-radiation-computed tomography (SRCT) allows more accurate calcified plaque and coronary stenosis assessment as a result of its superior spatial resolution; however, typical micro-computed tomography (micro-CT) systems have even higher resolution. The purpose of this study was to compare the performance of high-resolution micro-CT with SRCT in the assessment of calcified plaques and a previously published dataset of coronary stenosis assessment. This experimental study involved micro-CT scanning of three-dimensional printed coronary artery models with calcification in situ used in our previously published SRCT study on coronary stenosis assessment. Measurements of coronary stenosis utilizing both modalities were compared using a paired sample t-test. The degrees of stenosis measured on all but one micro-CT dataset were statistically significantly lower than the corresponding SRCT measurements reported in our previous paper (p < 0.0005–0.05). This indicates that the superior spatial resolution of micro-CT was able to further reduce over-estimation of stenosis caused by extensive calcification of coronary arteries and, hence, false positive results. Our results showed that the high-resolution micro-CT used in this study outperformed the Australian Synchrotron SRCT in both calcified plaque and coronary stenosis assessment. These findings will become clinically important for cardiovascular event prediction and enable reclassification of individuals with low and intermediate risk into appropriate risk categories when the technical challenges of micro-CT in clinical practice such as the small field of view and demanding on image processing power are addressed.

Dual-energy computed tomography (DECT) is an imaging technique widely used in traumatic settings to diagnose bone marrow oedema (BME). This paper describes the role of DECT in diagnosing BME in non-traumatic settings by evaluating its reliability in analyzing some of the most common painful syndromes. In particular, with an illustrative approach, the paper describes the possible use of DECT for the evaluation of osteochondral lesions of the knee and of the ankle, avascular necrosis of the hip, non-traumatic stress fractures, and other inflammatory and infectious disorders of the bones.

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

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