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Objective: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. Materials and methods: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. Results: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver–spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver–spleen difference of −40 HU and −10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate–severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). Conclusion: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. Clinical relevance statement: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. Key Points: The liver–spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver–spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to show how the "Puncture Cube" (PC) (Medical Templates, Egg, Switzerland) compares to the freehand method (FHM) for CT-guided punctures. Methods: The PC is a patient-mounted disassemblable cube consisting of an upper and lower template with multiple holes each to predefine puncture trajectory. A total of 80 punctures (FHM in-plane, FHM off-plane, PC in-plane, PC off-plane) was performed by 4 radiologists on a target 9.1 cm below surface level of a neoprene covered elliptical cylinder gelatin phantom. The PC was never disassembled. Evaluated parameters were procedure time, number of CT-scans, euclidean distance (ED) and normal distance (ND). Respective parameters of FHM and PC were compared using the Wilcoxon signed-rank test and Levene test with significance levels of 5%. Results: PC achieved smaller ED and ND values after initial needle insertion without corrections for both in-plane and off-plane punctures (P > 0.05). Variance of initial NDs was off-plane significantly larger for FHM. Final ED after needle path corrections was smaller for FHM both in- and off-plane (P < 0.05). Final off-plane ND was significantly lower for FHM with no significant difference in final in-plane ND. FHM off-plane punctures were significantly faster. There was no significant difference in CT-scans between both methods. Conclusion: Utilizing the PC may improve initial needle positioning and safety especially off-plane. However, better final needle positioning after correction with the greater freedom of movement method may suggest need for disassembly of the cube [ABSTRACT FROM AUTHOR]

Purpose: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans. Methods: PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings. Results: Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection. Conclusions: The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost. [ABSTRACT FROM AUTHOR]

Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The augmentation approaches must enhance the model's performance during the learning period. There are several types of transformations that can be applied to medical images. These transformations can be applied to the entire dataset or to a subset of the data, depending on the desired outcome. In this study, we categorize data augmentation methods into four groups: Absent augmentation, where no modifications are made; basic augmentation, which includes brightness and contrast adjustments; intermediate augmentation, encompassing a wider array of transformations like rotation, flipping, and shifting in addition to brightness and contrast adjustments; and advanced augmentation, where all transformation layers are employed. We plan to conduct a comprehensive analysis to determine which group performs best when applied to brain CT images. This evaluation aims to identify the augmentation group that produces the most favorable results in terms of improving model accuracy, minimizing diagnostic errors, and ensuring the robustness of the model in the context of brain CT image analysis. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine optimal radiological parameters for assessment of the round window approach in cochlear implantation surgery. Materials and methods: Patients undergoing cochlear implantation at the Department of Otolaryngology in Szczecin, between 2015 and 2022 inclusive, were eligible for the study. Radiological assessments were performed according to eight parameters (seven proposed in the literature) and visibility clinical assessments were made intra-operatively on a scale of 1 to 5 (1 - not visible, 5 - fully visible). Visibility assessments of the round window niche (RWN) and round window membrane (RWM) allowed the difference (RWN minus RWM) to be used as a clinical assessment of the size of the overhang over the round window. Results: Computed tomography images of 57 ears from 52 patients were analyzed in terms of round window access. The study group included 26 females and 26 males, ranging in age from 1 year to 80 years, with a median age of 41 years. In clinical assessment, round window visibility was rated as 5, after removal of the bone overhang, in 69% of patients. Cochlear access through the round window was achieved in 39 (68%) cases, extended access through the round window in 13 (23%) cases and cochleostomy was performed in 5 (9%) cases. Statistically significant ordinal correlations with round-window access were found using one parameter from the literature (Chen_Angle) and from our proposal (RWM_prediction). From parameters describing the bone overhang of the round window, positive correlations (using Kendall rank tests) were found using parameters from the literature (Sarafraz_OH and Mehanna_OH). Conclusions: Radiological measurements describing access to the round window which determine the angle based on the anatomy of the posterior wall of the auditory canal and the position of the facial nerve were found to be of the highest value. Clinical relevance statement: In the future, the use of algorithms for computed tomography evaluation and robot-assisted surgery will require parameters for assessing round window access, for surgery planning and choice of electrode. The parameters proposed by various authors are summarized, allowing researchers to assess their usefulness in further clinical practice. [ABSTRACT FROM AUTHOR]

Lung cancer is currently the leading cause of cancer-related death. To lower the mortality from lung cancer, early detection is crucial. A precise and effective method of diagnosis by medical professionals is necessary for early detection of lung-related cancers in order to maximize the success rate of treatment. It is particularly difficult to catch it in the early stages of dissemination because there are no symptoms in the early stages. Using specific image processing ideas on computed tomography (CT), we can identify the tumor state in the early phases of spread and diagnose this at an early level. The early detection is crucial for curing and restraining the spread of uncontrolled malignant cells. The best outcomes for accurately identifying malignant tumors in their early stages come from combining the effects of morphological filtering and a contour-based picture segmentation technique. The segmented lung CT scan pictures that were malignant were separated from the rest of the testing dataset by a support vector machine (SVM) classifier in the subsequent analysis phase. Various image quality and performance metrics, such as accuracy, sensitivity, precision and specificity are used to validate the proposed technique. [ABSTRACT FROM AUTHOR]

The commonly used automatic segmentation algorithms often have over-segmentation and error-segmentation problems when segmenting conglutinated bone fragments in computed tomography (CT) images. And the end face details of the fragments are lost in this process. At present, bone fragments are often manually segmented by doctors. This increases the workload of doctors and takes low efficiency and poor accuracy in bone segmentation. We propose an automatic bone fragment segmentation algorithm based on morphology to segment the conglutinated and separated bone fragments and reconstruct them in 2D and 3D spaces. The pixel classification and clustering method based on morphology are proposed in in the algorithm to identify the pixels in the fracture images. And a concave points detection method is proposed to segment the conglutination area of the fractured bone. This algorithm and two other commonly used algorithms are used in the experiment to segment the three typical fracture images. The segmentation results are quantitatively analyzed to demonstrate the advantages of the morphology algorithm. The morphology algorithm performs well in the metrics of Accuracy (.93 ±.07), Sensitivity (.92 ±.08), Dice coefficient (.92 ±.08) and Mean intersection over union (MIoU) (.92 ±.07). The segmentation results of this algorithm are closest to the manual segmentation results of doctors compared to the other two algorithms. We have demonstrated that the algorithm proposed in this paper has good segmentation accuracy for separated and conglutinated bone fragments. This algorithm significantly improves the segmentation accuracy and efficiency of bone fragments under complex fracture conditions. [ABSTRACT FROM AUTHOR]

Objective: This study investigates the importance of bone density, surface area, and diameter of anatomical structures of the superior semicircular canal (SSC), lateral semicircular canal (LSC), posterior semicircular canal (PSC), utricle, and saccule in patients diagnosed with superior semicircular canal dehiscence (SSCD). Materials and methods: The bone density, surface area, and diameter of SSC, LSC, PSC, utricle, and saccule were measured and compared between the SSCD group and control group. Fifteen ears in the SSCD group and 60 ears in the control group were evaluated. Additionally, within the SSCD group, the dehiscent and healthy sides were evaluated independently. Results: SSC's bone density was significantly lower in the SSCD group compared to the control group (p = 0.008). No significant differences were found in surface area and diameter between the groups (p > 0.05). While most of the anatomical structures showed no significant difference in bone density between dehiscent and healthy ears (p > 0.05), SSC bone density was significantly lower in affected ears (p = 0.000) in SSCD group. Conclusion: Based on the data obtained in this study, bone density and anatomical structure may be useful in patients diagnosed with SSCD. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate an iodine maps-based radiomics nomogram for preoperatively predicting cervical lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC). Materials and methods: A total of 278 patients who pathologically confirmed as HNSCC were retrospectively recruited from two medical centers between June 2012 and July 2022. The training set (n = 152) and internal set (n = 67) were randomly selected from medical center A, and the patients from medical center B were enrolled as the external set (n = 69). The minority group in the training set was balanced by the adaptive synthetic sampling (ADASYN) approach. Radiomics features were extracted from dual-energy CT-derived iodine maps at arterial phase (AP) and venous phase (VP), respectively. Three radiomics signatures were constructed to predict the LNM by using a random forest algorithm. The independent clinical predictors for LNM were identified by multivariate analysis and combined with radiomics signatures to establish a radiomic–clinical nomogram. The performance of radiomic–clinical nomogram was evaluated with respect to its discrimination and clinical usefulness. Results: The AP–VP-incorporated radiomics model exhibited a great predictive performance for LNM prediction with an area under curve (AUC) of 0.885 (95% CI, 0.836–0.933) in ADASYN-training set and confirmed in all validation sets. The nomogram that incorporated AP–VP radiomics signatures, CT-reported LN status, and histological grades yielded AUCs of 0.920 (95% CI, 0.881–0.959) in ADASYN-training set, 0.858 (95% CI, 0.771–0.944) in internal validation, and 0.849 (95% CI, 0.752–0.946) in external validation, with good calibration in all cohorts (p > 0.05). Decision curve analyses indicated the nomogram was clinically useful. In addition, the predictive performance of clinical–radiomics nomogram was also validation in combing cohorts. Stratified analysis confirmed the stability of nomogram, particularly in group negative for CT-reported LNM. Conclusion: Clinical–radiomics nomogram based on iodine maps exhibited promising performance in predicting LNM and providing valuable information for making individualized therapy decisions. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the long-term outcomes of trans-mastoid plugging of superior semicircular canal dehiscence (SSCD), focusing on complicated cases. Methods: In this cohort study, we included all patients who underwent trans-mastoid plugging of SSCD between 2009 and 2019. We evaluated the symptoms (autophony, sound-/pressure-induced vertigo, disequilibrium, aural fullness and pulsatile tinnitus) before and 1 year after surgery in the medical records. We systematically assessed the current symptoms 6.2 ± 3 years postoperative (range 2.2–12.3 years) using questionnaires sent by post and validated by telephone interviews. We also documented any complications and the need for further procedures. We compared pure tone and speech audiometry before and 1 year after surgery. Finally, the degree of mastoid pneumatisation and mastoid tegmen anatomy were reviewed on preoperative CT scans. Results: We included 24 ears in 23 patients. No complications were recorded, and none required a second procedure for SSCD. Following surgery, oscillopsia and Tullio phenomena resolved in all patients. Hyperacusis, autophony, and aural fullness were also settled in all patients except one. Balance impairment persisted to some degree in 35% of patients. No deterioration over the years was reported regarding the above symptoms. On average, bone conduction pure tone average pre- and 1 year postoperative were 13.7 ± 17 and 20.5 ± 18 dB, respectively (P = 0.002). Air bone gaps were reduced from 12.7 ± 8 to 5.9 ± 6 (P = 0.001). Two patients had a significant sclerotic mastoid, three had a prominent low-lying mastoid tegmen, and two had both. Anatomy had no effect on outcome. Conclusion: Trans-mastoid plugging of SSCD is a reliable and effective technique which achieves long-lasting symptom control, even in cases with sclerotic mastoid or low-lying mastoid tegmen. [ABSTRACT FROM AUTHOR]

In medical image processing, noise reduction is a particularly difficult problem to solve. Denoising can aid doctors in making a diagnosis of sickness. Due to statistical uncertainty in all physical measurements used in computed tomography, noise is unavoidably injected into CT images. To improve the quality of CT images, edge-preserving denoising methods and noise reduction techniques are needed. If the noise in low-draught CT pictures can be reduced or eliminated, then it should be able to boost its effectiveness without raising the draught. As a result, the extraction method used in this research is known as the optimized bilateral filter, and wavelet-based packet thresholding. Levy based rat prey catching optimization (LRPSO) is proposed to optimize the weight function of bilateral filtering. The denoising technique is employed to safeguard the edges and get rid of the noise. The proposed methodology's results are analyzed and contrasted using certain established methods. According to the differentiated outcome analysis, the Proposed Methodology's execution is finer and more acceptable to the existing procedures in terms of optical standard PSNR, SSIM, and Entropy Difference (ED). The PSNR of the projected model for 25 images, under CT1, CT2, CT3 and CT4 database is 27.92, 26.02, 26.46 and 26.78, respectively. [ABSTRACT FROM AUTHOR]

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Purpose: To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes. Methods: Data of 958 patients with clinical T1b–T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan–Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). Results: RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan–Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09–5.79, p = 0.031 and HR 3.71, 95% CI 1.58–8.73, p = 0.003, respectively), but not for overall death or RCC-specific death. Conclusions: A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b–T2 RCC preoperatively. [ABSTRACT FROM AUTHOR]

For many years, the relationship between permeability and temperature of rocks after high temperature was described by a classical exponential model based on thermos-elastic theory. However, recent experiments have shown that the thermal damage incurred by rocks after high temperatures is irreversible plastic damage. This paper aims to propose a new approach for creating a temperature-dependent permeability model that considers this irreversibility of thermal damage. To achieve this, energy dissipation theory and fractal theory were used to develop a model that describes the irreversible thermal damage evolution and temperature-dependent permeability of rocks after high temperature. The new model was verified through CT and gas permeability experiments. The results indicate that: 1. in the temperature range of 200 ~ 800 ℃, thermal damage develops into an irreversible plastic state due to energy dissipation; 2. the thermal damage of granite exhibits a porous structure at 50 ~ 600 ℃, which turns into a mixed pore-fracture structure at 600 ~ 800 ℃; 3. the granite matrix followed fractal distribution patterns after thermal treatment, demonstrating a quadratic correlation with temperature; 4. based on the fractal evolution of granite after high temperature, the temperature permeability model is derived. The new model is compared to the two commonly used models. The new model showed smaller fitting relative errors, and produced a better representation of the causes of plastic thermal damage, offering a promising solution for similar problems. Highlights: The classical exponential model between temperature and permeability is based on thermos-elastic theory. But in this paper, the development of plastic irreversible thermal damage is explained by energy dissipation Fractal theory is used to describe the evolution of thermal damage quantitatively Based on the fractal expression of thermal damage, a new temperature-dependent permeability model is deduced The fractal expression of thermal damage and the new temperature-dependent permeability model are verified by meso-CT test and gas permeability test [ABSTRACT FROM AUTHOR]

Background: Only verbal pregnancy screening is recommended for post-menarcheal females undergoing pelvic radiographs. In contrast, usually, a urine/serum pregnancy test for pelvic computed tomographic (CT) exams is required out of concern for higher radiation exposure. Objective: To estimate patient-specific fetus absorbed dose to a potentially pregnant minor from an optimized dose CT of the pelvis for femoral version and surgical planning and provide evidence that such examinations of the pelvis can be performed with only verbal pregnancy screening. Methods and methods: A retrospective study was performed on 102 female patients between 12–18 years of age (15.4 ± 2.1 years) who underwent optimized dose CT of the pelvis for orthopedic evaluation of femoral version and surgical planning. Optimized CT exams were performed with weight-adjusted kVp and tube current modulation. Patient-specific dose from the optimized dose CT was calculated using the National Cancer Institute Dosimetry System for CT (NCICT) database by matching each patient to a phantom from the NCI non-reference phantom library based on patient sex, weight, and height. The calculated absorbed uterus dose was used as a surrogate for the fetus dose. Furthermore, patient-specific organ doses were used to estimate the effective dose. The strengths of the linear relationships between the dose metrics and patient characteristics were assessed using Pearson correlation coefficients through linear regression. Results: The mean patient-specific effective dose for an optimized dose CT of the pelvis was 0.54 ± 0.20 mSv (range: 0.15–1.22 mSv). The mean estimated absorbed uterine dose was 1.57 ± 0.67 mGy (range: 0.42–4.81 mGy). Both effective dose and estimated uterine dose correlated poorly with patient physical characteristics (R = -0.26; 95% CI: [-0.43, -0.007] for age, R = 0.03; 95% CI: [-0.17, 0.22] for weight) but correlated strongly (R = 0.79, 95% CI: [0.7, 0.85]) with CTDIvol. Conclusion: The estimated fetus dose in case of pregnancy was significantly lower than 20 mGy for urine/serum pregnancy screening, suggesting that the pregnancy screening protocols in minors undergoing optimized dose CT require reassessment and may be safely performed by verbal attestation only. [ABSTRACT FROM AUTHOR]

Objectives: Symmetry is essential for computer-aided surgical (CAS) procedures in oral and maxillofacial surgery (OMFS). A critical step for successful CAS is mirroring the unaffected side to create a template for the virtual reconstruction of the injured anatomical structure. The aim was to identify specific anatomical landmarks of the midfacial skeleton, to evaluate the symmetry in a group of the real-world Central European population, and to use these landmarks to assess midfacial symmetry in CT scans. Material and methods: The retrospective cross-sectional study defined landmarks of the midface's bony contour using viscerocranial CT data. The distances of the skeletal landmarks (e.g., the frontozygomatic suture and temporozygomatic suture) of the left and right sides from the midline were measured and statistically compared. Midfacial symmetry for reference points was defined as a difference within 0 mm and their mean difference plus one standard deviation. Results: We examined a total of 101 CT scans. 75% of our population shows symmetrical proportions of the midface. The means of the differences for the left and right sides ranged from 0.8 to 1.3 mm, averaging 1.1 ± 0.2 mm for all skeletal landmarks. The standard deviations ranged from 0.6 to 1.4 mm, with a computed mean of 0.9 ± 0.3 mm. Conclusion: We established a methodology to assess the symmetry of the bony midface. If the determined differences were equal to or lower than 2.5 mm in the mentioned midfacial skeletal landmarks, then the symmetry of the bony midface was considered present, and symmetry-based methods for CAS procedures are applicable. Clinical relevance: Many CAS procedures require facial symmetry. We provide an easy-to-apply method to probe for symmetry of the midface. The method may be used for population-based research, to check for proper reduction of fractures after reposition or to screen for symmetry prior to CAS planning. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has caused great havoc and affected many parts of the world. It has imposed a great challenge to the medical and health fraternity with its ability to continue mutating and increasing the transmission rate. Some challenges include the availability of current knowledge of active drugs against the virus, mode of delivery of the medicaments, its diagnosis, which are relatively limited and do not suffice for further prognosis. One recently developed drug delivery system called nanoparticles is currently being utilized in combating COVID-19. This article highlights the existing methods for diagnosis of COVID-19 such as computed tomography scan, reverse transcription-polymerase chain reaction, nucleic acid sequencing, immunoassay, point-of-care test, detection from breath, nanotechnology-based bio-sensors, viral antigen detection, microfluidic device, magnetic nanosensor, magnetic resonance platform and internet-of-things biosensors. The latest detection strategy based on nanotechnology, biosensor, is said to produce satisfactory results in recognizing SARS-CoV-2 virus. It also highlights the successes in the research and development of COVID-19 treatments and vaccines that are already in use. In addition, there are a number of nanovaccines and nanomedicines currently in clinical trials that have the potential to target COVID-19. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [ABSTRACT FROM AUTHOR]

Objectives: To identify the earliest sites and spread patterns of bony erosions on high-resolution temporal bone computerized tomographic (HRTBCT) scans and conjugate the radiological findings with clinical/laboratory data for guiding the interpretation of CT images of skull base osteomyelitis (SBOM) from malignant otitis externa (MOE). Study design: Retrospective and prospective medical records analysis. Methods: The medical records of all patients who presented to a referral medical center during 2015–2022 and initially diagnosed with SBOM. Radiological assessment of each sub-site was performed by 2 qualified neuro-radiologist, who were blind to the clinical data. Results: 33 patients were enrolled. All patients suffered edema and exudates of the external auditory canal (EAC). The most common osseous markers for SBOM were dehiscence or irregularity of the anterior EAC wall and temporomandibular joint (TMJ) medial wall (21/33, 64% patients, each), followed by a fistula from the TMJ to the EAC (20/33, 61% patients). Eustachian tube bony walls and condylar irregularity and/or dehiscence were as prevalent as 16/33, 48% of patients, each. EAC inferior and posterior walls of the EACs were irregular or dehiscent in patients with more advanced disease. Conclusions: The high symptomatologic overlap between SBOM and other aural pathologies might cause diagnostic (and subsequent treatment) delay and result in dire complications. EAC edema and irregularity or dehiscence of the anterior EAC wall are the most common early CT findings associated with SBOM caused by MOE. Level of evidence: 4. [ABSTRACT FROM AUTHOR]

Objectives: To predict tumor grade (G1 vs. G2/3), presence of distant metastasis (M+), metastatic lymph nodes (N+), and microvascular invasion (VI) of pancreatic neuroendocrine neoplasms (PanNEN) based on preoperative CT radiomic features (RFs), by applying a machine learning approach aimed to limit overfit. Methods: This retrospective study included 101 patients who underwent surgery for PanNEN; the entire population was split into training (n = 70) and validation cohort (n = 31). Based on a previously validated methodology, after tumor segmentation on contrast-enhanced CT, RFs were extracted from unenhanced CT images. In addition, conventional radiological and clinical features were combined with RFs into multivariate logistic regression models using minimum redundancy and a bootstrap-based machine learning approach. For each endpoint, models were trained and validated including only RFs (RF_model), and both (radiomic and clinicoradiological) features (COMB_model). Results: Twenty-five patients had G2/G3 tumor, 37 N+, and 14 M+ and 38 were shown to have VI. From a total of 182 RFs initially extracted, few independent radiomic and clinicoradiological features were identified. For M+ and G, the resulting models showed moderate to high performances: areas under the curve (AUC) for training/validation cohorts were 0.85/0.77 (RF_model) and 0.81/0.81 (COMB_model) for M+ and 0.67/0.72 and 0.68/0.70 for G. Concerning N+ and VI, only the COMB_model could be built, with poorer performance for N+ (AUC = 0.72/0.61) compared to VI (0.82/0.75). For all endpoints, the negative predictive value was good (≥ 0.75). Conclusions: Combining few radiomic and clinicoradiological features resulted in presurgical prediction of histological characteristics of PanNENs. Despite the limited risk of overfit, external validations are warranted. Key Points: • Histology is the only tool currently available allowing characterization of PanNEN biological characteristics important for prognostic assessment; significant limitations to this approach exist. • Based upon preoperative contrast-enhanced CT images, a machine learning approach optimized to favor models' generalizability was successfully applied to train predictive models for tumor grading (G1 vs. G2/3), microvascular invasion, metastatic lymph nodes, and distant metastatic spread. • Moderate to high discriminative models (AUC: 0.67–0.85) based on few parameters (≤ 3) showing high negative predictive value (0.75–0.98) were generated and then successfully validated. [ABSTRACT FROM AUTHOR]

In 2017, our tertiary hospital-based imaging practice transitioned from score-based peer review to the peer learning methodology for learning and improvement. In our subspecialized practice, peer learning submissions are reviewed by domain experts, who then provide feedback to individual radiologists, curate cases for group learning sessions, and develop associated improvement initiatives. In this paper, we share lessons learned from our abdominal imaging peer learning submissions with the assumption that trends in our practice likely mimic others', and hope that other practices can avoid future errors and elevate the level of the quality of their own performance. Adoption of a nonjudgmental and efficient method to share peer "learning opportunities" and "great calls" has increased participation in this activity and increased transparency into our practice, thus allowing for visualization of trends in performance. Peer learning allows us to bring our own individual knowledge and practices together for group review in a collegial and safe environment. We learn from each other and decide how to improve together. [ABSTRACT FROM AUTHOR]

Purpose: The objective of the study was to evaluate the proposed cochlear duct length estimation based on the cochlear 'A value'. Furthermore, we assessed the interobserver variability between radiology and otolaryngology attending physicians and otolaryngology trainees. Methods: Thirteen pediatric cochlear implant candidates were retrospectively analyzed by three otolaryngology physicians (attending physician, second year, and fourth year trainees) and a radiology attending. The cochlear duct length was calculated based on the formula of Grover et al. The differences in acquired measurements between observers were compared using the Wilcoxon matched signed-rank test. Results: The differences in measurements between the attending otolaryngologist and radiologist were not statistically different, while several significant differences were observed with regard to measurements of attending doctors compared to both residents. In particular, a significant difference between the second year otolaryngology resident and otolaryngology and radiology attending was observed for one side (right ear p = 0.034 and p = 0.012, respectively). Moreover, the fourth year resident calculated significantly different cochlear duct measurements when compared to the attending otolaryngologist (left ear p = 0.014) and radiologist (right ear p = 0.047). Interestingly, differently experienced otolaryngology residents provided significantly different measurements for both ears. Conclusions: Based on these results, cochlear duct length measurement according to the proposed method may be a reliable and cost-effective method. Indeed, otolaryngology training may be sufficient to provide measurements comparable to radiologists. On the other hand, additional efforts should be invested during otolaryngology training in terms of the evaluation of radiological imaging which may increase the capabilities of otolaryngology residents in this regard. [ABSTRACT FROM AUTHOR]

In electron-beam welding, stability of the beam's spatial characteristics is essential in order to ensure weld quality. For diagnostics and determination of the beam's spatial and energy characteristics, a Faraday cup is employed, in combination with computer tomography. In determining the current density distribution of the electron beam from the set of projections recorded, Fourier methods are generally adopted. Deficiencies of such methods include poor numerical stability and applicability to a very limited class of objects. To accelerate data analysis, increase the accuracy, and decrease the noise of the measurements, an algebraic approach is proposed, in conjunction with synchronous data accumulation. An algorithm is developed for algebraic derivation and reconstruction of the energy density distribution and monitoring of beam focusing. The two-dimensional current density distribution obtained by analysis is used to obtain the parameters of beam focusing, such as the diameter calculated from the width of the energy density distribution at characteristic levels; the area of the distribution curve or the null central moment of the distributed normalized by the maximum distribution density; and the effective diameter, indicating the interval in which 68% of the beam energy is incident. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma. Methods: A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test. Results: The proposed DL signature yielded an AUC of 0.948–0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature. Conclusions: The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options. Key Points: • Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948–0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature. [ABSTRACT FROM AUTHOR]

Electron-beam welding may be introduced in various industries, such as instrument production, the auto and aerospace industries, and shipbuilding, for the production of metal and alloy components where weld quality is critical. It offers the potential for automation and for vacuum welding, so as to ensure high purity of the material. In addition, the welding energy is concentrated within an electron beam, which permits the production of deep and narrow weld seams, with very localized heating. The distribution of the electron beam's energy density is critical to its interaction with the workpiece; it directly affects numerous secondary physical effects and hence the weld quality. A device based on computer tomography is proposed for the diagnostics of electron-beam guns and also the elimination of errors due to approximation of the beam in modeling and setting up the welding process. By means of the proposed device, the actual spatial and energy characteristics of the electron beam may be determined. [ABSTRACT FROM AUTHOR]

The change in three-dimensional structure of a polymer composite component under the influence of wire-cut electrical discharge machining (EDM) is investigated. Individual cavities and defects appear in the uniform and oriented structure of a woven composite during 3D printing. During EDM, a pattern of surface cavities, cracking, chipping, and peeling of the composite fabric is observed, with in the regions where material depth is 1 mm. Such surface and subsurface heterogeneity of the material due to EDM may result in premature failure of the component during its use. [ABSTRACT FROM AUTHOR]

One factor that limits development of fundamental research on the influence of coke microstructure on its strength is the difficulty in quantifying the way that microstructure is both classified and distributed in three dimensions. To support such fundamental studies, this study evaluated a novel volumetric approach for classifying small (approx. 450 μm3) blocks of coke microstructure from 3D computed tomography scans. An automated process for classifying microstructure blocks was described. It is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means clustering. The approach was evaluated using 27 coke samples across a range of coke with different properties and reliably identified 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural density. The lower class (1–2) subblocks tend to be dominated by pores and thin walls. Typically, there is an increase in wall thickness and reduced pore sizes in the higher classes. Inert features are also likely to be seen in higher classes (5–6). In general, this approach provides an efficient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke. [ABSTRACT FROM AUTHOR]

One of the crucial problems in medical field is liver cancer, which creates a huge impact on the mortality rate. Though, existing histopathological diagnostic approaches pose more trouble on the medical detection model due to the complexities in the workload. Identifying the tumor and liver regions from the clinical CT images through complete automatic diagnosis process is crucial for liver disease detection. Hence, by combining deep learning with CT images, this paper implements a new model to increase the efficiency of the liver tumor diagnosis. The benchmark and manually collected datasets are initially taken for pre-processing performed by histogram equalization and median filtering. Further, the segmentation of the liver is done by adaptive thresholding with level set segmentation. Once the liver segmentation is done, the enhanced deep learning method termed U-Net is adopted for segmenting the tumor using a new Grey Wolf-Class Topper Optimization (GW-CTO) algorithm. Further, a set of features are extracted and the length of features leads to complexity in network training, so the optimal feature selection is adopted based on a multi-objective function by the GW-CTO algorithm. These optimally selected features are subjected to the Hyper-parameter tuned Improved-Deep Neural Network (HI-DNN) enhanced by the same GW-CTO algorithm. From the performance analysis, the accuracy of the developed GW-CTO-HI-DNN is 4.3%, 2.4%, 5.2% and 4.3% progressed than PSO-HI-DNN, O-SHO-HI-DNN, CTO-HI-DNN and GWO-HI-DNN, respectively while considering the learning percentage as 85%. The experimental analysis confirms the efficiency of the developed model to get high classification accuracy over the other methods. [ABSTRACT FROM AUTHOR]

Acute appendicitis is one of the most common general surgical emergencies worldwide; however, its diagnosis remains challenging, with a high proportion of negative appendicectomies. The purpose of this study was to investigate the benefit of routine use of pre-operative imaging for the evaluation of suspected appendicitis. This retrospective cohort study included all cases of appendicectomies performed for suspected acute appendicitis during the first and second peaks of the COVID-19 pandemic, between March 2020 and February 2021. The control group included all cases of appendicectomies performed for suspected acute appendicitis in the previous 12 months (March 2019-February 2020). One hundred and four patients underwent appendicectomy in the study group, compared to 209 in the control group, with similar gender distribution but a significantly higher median age in the study group (33 vs. 28, p = 0.001). The two groups had similar rates of perforation and similar median white cell count (WCC) and CRP. Imaging was used in 80.77% of the patients in the study group, compared to 61.72% in the control group (p = 0.001), with 55.77% of patients in the study group undergoing CT scans. Despite this, the negative appendicectomy rate (NAR) in the two groups did not differ significantly (11.54% vs. 15.79%, p = 0.320). The increase in the use of imaging for the diagnosis of acute appendicitis during the COVID-19 pandemic did not lead to a significantly lower negative appendicectomy rate. Registration: The study was pre-registered at ClinicalTrials.gov (NCT05205681). [ABSTRACT FROM AUTHOR]

Objectives: Investigate the laboratory, imaging and procedural factors that are associated with a tumour-positive and/or NGS-feasible CT-guided sclerotic bone lesion biopsy result in cancer patients.Methods: In total, 113 CT-guided bone biopsies performed in cancer patients by an interventional radiologist in one institution were retrospectively reviewed. Sixty-five sclerotic bone biopsies were eventually included and routine blood parameters and tumour marker levels were recorded. Non-contrast (NC) biopsy CTs (65), contrast-enhanced CTs (24), and PET/CTs (22) performed within four weeks of biopsy were reviewed; lesion location, diameter, lesion-to-cortex distance, and NC-CT appearance (dense-sclerosis versus mild-sclerosis) were noted. Mean NC-CT, CE-CT HU, and PET SUVmax were derived from biopsy tract and lesion segmentations. Needle diameter, tract length, and number of samples were noted. Comparisons between tumour-positive/negative and next-generation sequencing (NGS)-feasible/non-feasible biopsies determined significant (p < 0.05) laboratory, imaging, and procedural parameter differences.Results: Seventy-four percent of biopsies were tumour-positive. NGS was feasible in 22/30 prostate cancer patients (73%). Neither laboratory blood parameters, PET/CT availability, size, nor lesion-to-cortex distance affected diagnostic yield or NGS feasibility (p > 0.298). Eighty-seven percent of mildly sclerotic bone (mean 244 HU) biopsies were positive compared with 56% in dense sclerosis (622 HU, p = 0.005) and NC-CT lesion HU was significantly lower in positive biopsies (p = 0.003). A 610 HU threshold yielded 89% PPV for tumour-positive biopsies and a 370 HU threshold 94% PPV for NGS-feasible biopsies. FDG-PET and procedural parameters were non-significant factors (each p > 0.055).Conclusion: In cancer patients with sclerotic bone disease, targeting areas of predominantly mild sclerosis in lower CT-attenuation lesions can improve tumour tissue yield and NGS feasibility.Key Points: • Areas of predominantly mild sclerosis should be preferred to areas of predominantly dense sclerosis for CT-guided bone biopsies in cancer patients. • Among sclerotic bone lesions in prostate cancer patients, lesions with a mean HU below 370 should be preferred as biopsy targets to improve NGS feasibility. • Laboratory parameters and procedure related factors may have little implications for CT-guided sclerotic bone biopsy success. [ABSTRACT FROM AUTHOR]

Design principles for a smart additive manufacturing system are considered, along with its goals and technical profile. The system is based on high-speed spiral X-ray computer tomography with continuous nondestructive real-time quality control and is intended to generate products of high value and reliability. The technical profile of the smart system is developed by preliminary review of the potential applications of artificial intelligence in enhancing the quality of additive manufacturing. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review provides the summary of the appropriate use of these modalities when caring for patients with Kawasaki disease at diagnosis and for long-term management. Recent Findings: Kawasaki disease is an inflammatory syndrome of unknown etiology that can result in coronary artery dilations or aneurysms if left untreated in 25% of the patients and 3–5% in treated patients. In addition to coronary artery aneurysms, patients can have ventricular dysfunction, valvular regurgitation, aortic root dilation, and pericardial effusion due to inflammation of the myocardium. Noninvasive imaging modalities are important to these assessments. Summary: Echocardiography is the first-line noninvasive evaluation of coronaries and function. Cardiac magnetic resonance imaging is useful for functional assessment in long-term follow-up. Distal coronaries, thromboses, and stenoses are best evaluated by cardiac computed tomography. Future research should demonstrate the effectiveness of advanced functional imaging in patients with Kawasaki disease and decreased radiation from cardiac computed tomography. [ABSTRACT FROM AUTHOR]

Objective: To develop a simple scoring system in order to predict the risk of severe (death and/or surgery) ischemic colitis METHODS: In this retrospective study, 205 patients diagnosed with ischemic colitis in a tertiary hospital were consecutively included over a 6-year period. The study sample was sequentially divided into a training cohort (n = 103) and a validation cohort (n = 102). In the training cohort, multivariable analysis was used to identify clinical, biological, and CT variables associated with poor outcome and to build a risk scoring system. The discriminative ability of the score (sensitivity, specificity, positive predictive value, negative predictive value) was estimated in the two cohorts to externally validate the score, and a receiver operating characteristic curve was established to estimate the area under the curve of the score. Bootstrapping was used to validate the score internally.Results: In the training cohort, four independent variables were associated with unfavorable outcome: hemodynamic instability (2 pts), involvement of the small bowel (1 pt), paper-thin wall pattern (3 pts), no stratified enhancement pattern (1 pt). The score was used to categorize patients into low risk (score: 0, 1), high risk (score: 2-3), and very high risk (score: 4-7) groups with sensitivity and specificity of 97% and 67%, respectively, and a good discriminating capability, with a C-statistic of 0.94. Internal and external validation showed good discrimination capability (C-statistics of 0.9 and 0.84, respectively).Conclusion: A simple risk score can stratify patients into three distinct prognosis groups, which can optimize patient management.Clinical Trial Number: NCT04662268 KEY POINTS: • Simple scoring system predicting the risk of severe ischemic colitis • First study to include CT findings to the clinical and biological data used to determine a severity score. [ABSTRACT FROM AUTHOR]

Purpose: Adipokines have been associated with increased risk of cardiovascular disease. Our aim was to determine if adipokine levels are associated with coronary artery calcification (CAC) as well as all-cause mortality in incident dialysis patients. Methods: In patients new to dialysis, we prospectively investigated the association of adiponectin, leptin and resistin with coronary artery calcification measured by ECG-gated computer tomography. Participants were recruited a median of two months after starting dialysis. Results: The mean age was 50.0 (12.6) years and 31.1% were women. About 42% percent had BMI > 30. Higher adiponectin levels were inversely associated with CAC progression as change in Agatston score [−155.1 (−267.9, −42.2), p = 0.008] or change in CAC volumes between scans [−2.8 (−4.9, −0.6), p = 0.01]. Higher leptin levels were associated with CAC progression [110.4 (34.3–186.6), p = 0.005]. Decreased leptin [HR 0.5 (0.3–0.9), p = 0.05] was associated with all-cause mortality in adjusted models. There was no significant association between all-cause mortality and adiponectin [1.4 (0.6–3.4), p = 0.4] or resistin [HR 1.7 (0.5–5.0), p = 0.4]. Conclusion: High adiponectin protects against CAC progression, but is not associated with increased all-cause mortality. Higher leptin, as well as higher leptin to adiponectin ratio, is associated with CAC progression. Lower leptin levels were associated with all-cause mortality. The association of adipokines and cardiovascular disease in individuals on dialysis is complex and requires further study. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the effect of hepatic metastatic lesion size on inter-reader reproducibility of CT-based 2D radiomics imaging features. Methods: Computerized tomography (CT) scans of 59 liver metastases from 34 patients with colorectal cancer were evaluated. Image segmentation was performed manually by three readers blinded to each other's results. For each radiomics feature, we created two datasets by sorting measurements according to size, i.e., (i) from the smallest to the largest lesion and (ii) from the largest to the smallest lesion. The Lin concordance correlation coefficient (CCC) was employed to analyze the reproducibility of radiomics features. In particular, the CCC was computed as a function of a number of elements in the dataset, by gradually adding lesions from each sorted dataset. To evaluate the effect of lesion size, we analyzed the difference between these two functions thus assessing the contribution of small and large lesions into the reproducibility of radiomics features. Results: Inter-reader reproducibility of CT-based 2D radiomics features assessed using Lin's CCC demonstrates tumor-size dependence. For example, the Lin CCC for GLCM contrast equals 0.88 (95% C.I. 0.84 to 0.92, p < 0.003) and could change by an additional + / − 0.06 depending on the presence of large or small lesions. Conclusions: Groups of "large" and "small" lesions show different inter-reader reproducibility. The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution and can vary widely. This finding could partially explain variability in reproducibility of radiomics features in the literature. Key Points: • Groups of "large" and "small" lesions show different inter-reader reproducibility. • The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution. [ABSTRACT FROM AUTHOR]

Introduction: To develop and validate a simple-to-use nomogram based on preoperative CT to predict spread through air space (STAS) status of stage IA lung adenocarcinoma (ADC). Methods: In this retrospective study, 434 patients with pathological proven periphery stage IA lung adenocarcinoma were included, which consisted of 349 patients from center I for training group and 85 patients from Center II for test group. STAS was identified in 53 patients (40 patient in the training group and 13 patients in the test group). On the basis of preoperative CT images, 19 morphological characteristics were analyzed. Univariable analysis was used to explore the association between clinical and CT characteristics and STAS status in the training group (P < 0.002). Independent risk factors for STAS were identified using multivariable logistic regression analysis and then used to build a nomogram for preoperative predicting STAS status. Results: Type of nodules, diameter of solid component, lobulation and percentage of the solid component (PSC) were associated with STAS status of peripheral stage IA lung ADCs statistical significantly. Multivariate logistics regression analysis revealed that PSC and lobulation were independent risk factors for STAS. The nomogram based on these factors achieved good predictive performance for STAS with a C-index of 0.803 in the training group and a well-fitted calibration curve. Using a cut-off value which was obtained from Youden index of the receiver operating characteristic (ROC) curve, a diagnosis accuracy of 70.6% was obtained in the test group with sensitivity, specificity, positive prediction value (PPV) and negative prediction value (NPV) of 92.3%, 66.7%, 33.3% and 98.0%, respectively. Conclusion: The nomogram based on preoperative CT images could achieve good predictive performance for STAS status of lung adenocarcinomas. This simple-to-used model can facilitate surgeons for a rational operation pattern choice at bedside. [ABSTRACT FROM AUTHOR]

This study explores the magnitude of two sources of error that are introduced when extracorporeal bullet trajectories are based on post-mortem computed tomography (PMCT) and/or surface scanning of a body. The first source of error is caused by an altered gravitational pull on soft tissue, which is introduced when a body is scanned in another position than it had when hit. The second source of error is introduced when scanned images are translated into a virtual representation of the victim's body. To study the combined magnitude of these errors, virtual shooting trajectories with known vertical angles through five "victims" (live test persons) were simulated. The positions of the simulated wounds on the bodies were marked, with the victims in upright positions. Next, the victims were scanned in supine position, using 3D surface scanning, similar to a body's position when scanned during a PMCT. Seven experts, used to working with 3D data, were asked to determine the bullet trajectories based on the virtual representations of the bodies. The errors between the known and determined trajectories were analysed and discussed. The results of this study give a feel for the magnitude of the introduced errors and can be used to reconstruct actual shooting incidents using PMCT data. [ABSTRACT FROM AUTHOR]

Purpose: Multislice spiral CT (MSCT) was used to investigate the anatomical characteristics of sternal development, and to provide anatomical basis for sternal puncture in children. Methods: We retrospectively analyzed the thoracic MSCT data of 600 children who received thoracic MSCT from January to June 2020 with their age ranging from 1 month to 19 years. The distribution of sternal ossification centers and adjacent tissues and organs was observed. Subcutaneous soft tissue thickness and the distance between the skin and the posterior margin of the sternum were measured in the central areas of sternal manubrium and mesosternum (segments I and II), and the correlation between the two was calculated using linear correlation. Results: A total of 600 patients were enrolled, the mean age was 9.87 years and the standard deviation was 8.28 years. The sternal manubrium and ossification centers at the I and II segments of the mesosternum were visible in all cases (100%). There was no ossification in segment III of the mesosternum in 15 cases (2.5%), including 12 cases (80%) adjacent to the posterior pericardium and 7 cases (46.7%) of lung tissue. There were 274 cases (45.7%) of segment IV without ossification, including 204 cases (74.5%) of adjacent pericardium and 95 cases (32.8%) of lung tissue. The xiphoid process was not ossified in 258 cases (43%), including 190 cases (73.6%) adjacent to the pericardium and 97 cases (37.6%) adjacent to the lung tissue. Correspondingly, the thickness of subcutaneous soft tissue of the sternal manubrium and the central region of the I and II segments of the mesosternum had a low positive correlation with age (P < 0.001), the distance between the skin and the posterior margin of the sternum showed a moderate positive correlation with age (P < 0.001), and the distance between the skin and the posterior margin of the sternum showed a high positive correlation with the thickness of subcutaneous soft tissue (P < 0.001). Conclusions: Nonossification of the sternal ossification center usually occurs below segment III of the mesosternum and is usually adjacent to heart and lung tissue. Pediatric sternal puncture should be performed at the sternal manubrium and the mesosternum of segments I and II. However, attention should be paid to the space between multiple ossification centers. The thickness of subcutaneous soft tissue is a critical factor that determines the depth of the puncture. [ABSTRACT FROM AUTHOR]

An efficient tomographic inversion model is very beneficial to image the ionosphere in real time. Geometric matrix determination, one of the indispensable procedures of voxel-based tomographic inversion, is currently very inefficient. We developed a very fast algorithm named splitting-surface intersection and voxel tracing (SIVT) to compute the geometric matrix. Instead of applying an intersecting voxel test and several intersection computations on a large number of voxels, SIVT calculates only the intersections of a ray with the splitting surfaces of a voxel model and determines the intersecting voxels by direct voxel tracing. Theoretically, the SIVT approach costs O(n log n) time (where n refers to the number of splitting surfaces (n)) compared to O(m) for the auxiliary plane (AP) approach (where m refers to the number of voxels and is always larger than n by several orders of magnitude). Our tests show that the SIVT approach outperforms the AP approach by approximately 100–3000 times, and the superiority of the SIVT approach increases exponentially as m or n grows. [ABSTRACT FROM AUTHOR]

Purpose: High-resolution temporal bone computer tomography (HRTBCT) is the most common initial radiological modality used for the assessment of necrotizing otitis externa (NOE). Objectives: (1) To compare the extension of disease, as seen on HRTBCT, in patients with NOE caused by different pathogens and (2) assess whether radiological findings may suggest the offending pathogen in cases of sterile-NOE. Methods: All NOE patients were hospitalized between 1990 and 2018. All patients underwent HRTBCT at admission. Three groups (fungus-NOE, PA-NOE and sterile-NOE) comprising of ten patients each were randomly selected. HRTBCT was reevaluated by a senior radiologist. Thirteen radiological subsites were selected for reevaluation. Results: All patients in the fungal-NOE group complained of otalgia, compared to nine in the sterile-NOE and six in the PA-NOE groups (p value = 0.044). External ear canal edema and granulation tissue were the most common findings in all groups. Surgery was performed in five patients in the fungal-NOE and PA-NOE and three in the sterile-NOE group (p value = > 0.05). Radiological findings indicating severe bone erosion within the EEC was seen in all patients but 3 (p value = > 0.05). Severe TMJ erosion was seen in one patient within the fungal-NOE and PA-NOE group (p value = > 0.05). When mild and severe involvement were combined, TMJ bone erosion was seen in four patients in the fungal-NOE and only in one patient in the PA-NOE (p = 0.04). Conclusions: TMJ involvement may be more common in fungal disease, suggesting a different spreading pathway, as opposed to PA-NOE. Accordingly, TMJ involvement on HRTBCT may justify antifungal treatment in sterile culture-NOE. [ABSTRACT FROM AUTHOR]

Purpose.: Due to its safe, low-cost, portable, and real-time nature, ultrasound is a prominent imaging method in computer-assisted interventions. However, typical B-mode ultrasound images have limited contrast and tissue differentiation capability for several clinical applications. Methods.: Recent introduction of imaging speed-of-sound (SoS) in soft tissues using conventional ultrasound systems and transducers has great potential in clinical translation providing additional imaging contrast, e.g., in intervention planning, navigation, and guidance applications. However, current pulse-echo SoS imaging methods relying on plane wave (PW) sequences are highly prone to aberration effects, therefore suboptimal in image quality. In this paper we propose using diverging waves (DW) for SoS imaging and study this comparatively to PW. Results.: We demonstrate wavefront aberration and its effects on the key step of displacement tracking in the SoS reconstruction pipeline, comparatively between PW and DW on a synthetic example. We then present the parameterization sensitivity of both approaches on a set of simulated phantoms. Analyzing SoS imaging performance comparatively indicates that using DW instead of PW, the reconstruction accuracy improves by over 20% in root-mean-square-error (RMSE) and by 42% in contrast-to-noise ratio (CNR). We then demonstrate SoS reconstructions with actual US acquisitions of a breast phantom. With our proposed DW, CNR for a high contrast tumor-representative inclusion is improved by 42%, while for a low contrast cyst-representative inclusion a 2.8-fold improvement is achieved. Conclusion.: SoS imaging, so far only studied using a plane wave transmission scheme, can be made more reliable and accurate using DW. The high imaging contrast of DW-based SoS imaging will thus facilitate the clinical translation of the method and utilization in computer-assisted interventions such as ultrasound-guided biopsies, where B-Mode contrast is often to low to detect potential lesions. [ABSTRACT FROM AUTHOR]

Technological regimes for producing wrought products (2 and 1 mm) from the Al–4.5%Zn–2.5%Mg–2.5%Ca–0.5%Fe–0.2%Zr–0.1%Sc experimental alloy, including thermomechanical processing at temperatures of 400–450°C and a reduction ratio up to 98%, as well as softening annealing of the sheet metal at 350–400°C for 1–2 h, are presented. It is found that the as-cast structure consists of eutectic phases (Al, Zn)4Ca, Al10CaFe2, and a nonequilibrium T-phase Al2Mg3Zn3 with a size from 5 to 25 μm located along the boundaries of dendritic cells (Al). Zirconium and scandium in solid solution out of solidification are observed. After hot rolling, the structure of 2-mm sheets consists of oriented discrete intermetallic particles and their conglomerates up to 40 μm in scale in the (Al) matrix. The structure of 1-mm sheets is characterized by greater fineness and uniformity of structure. An analysis of the fine structure of deformed semifinished products using transmission electron microscopy (TEM) shows that the size of nanoparticles of the Al3(Zr,Sc) phase of the L12 structural type does not exceed 20 nm in cross section. In wrought semifinished products, the following level of mechanical properties is achieved: ultimate strength σu ~ 310–330 MPa and yield strength σ0.2 ~ 250–280 MPa with a relative elongation δ ~ 4.5–7.0%. The possibility of using argon-arc welding with standard AMg5 wire as an additive material is studied. It is shown that the new alloy does not show a tendency to form hot cracks. According to the results of X-ray tomography, the percentage of porosity in the weld is 1.27 vol %. The prevalent pore diameter does not exceed 0.2 mm. In general, the resulting structural and qualitative parameters of welded joints contribute to obtaining a strength of 75% of the strength index of the initial deformed semifinished products (sheets), which is achieved by stabilizing annealing at a temperature of 350°C for 3 h. [ABSTRACT FROM AUTHOR]

Lung cancer is currently one of the most common causes of cancer-related death. Detecting and providing an accurate diagnosis of potentially cancerous lung nodules at an early stage of their development would increase treatment efficacy and so reduce lung cancer mortality. A key barrier to early detection is the absence of noticeable symptoms until the lung cancer has already spread. Diagnosis and screening using non-invasive imaging such as computed tomography (CT) is a potential solution. However, to realize the potential of this approach an accurate automated analysis of these high-resolution images needed. Image segmentation is an important stage of that process. Fuzzy-based image segmentation schemes use the maximum of each row and minimum of each column. Our study developed an algorithm that employs median values measured along each row and column, in addition to the maxima and minima values, and found that this approach increased segmenting accuracy of these images,. In the next phase of analysis, a neuro-fuzzy classifier classified those segmented lung nodules into malignant and benign nodules. Sensitivity, specificity and accuracy were used as performance assessment parameters. The proposed methodology resulted in sensitivity, specificity, precision and accuracy of 100%, 81%, 86% and 90%, respectively, with a reduced false positive rate. In sum, our improved algorithm can give significantly improved accuracy of diagnosis in early-stage patients from CT imaging. Thus, our methodology could contribute to better clinical outcomes for lung cancer patients. [ABSTRACT FROM AUTHOR]

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated. [ABSTRACT FROM AUTHOR]

Parts and billets produced from aluminum alloys and carbon steels using different methods of control, i.e., ultrasonic and radiographic inspection, computer tomography and metallographic analysis, are studied. Computer tomography is shown to be more advantageous than the other methods of nondestructive inspection. [ABSTRACT FROM AUTHOR]

Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference. [ABSTRACT FROM AUTHOR]

The ectoparasitic mite, Varroa destructor, is a most important health risk for European honey bee subspecies (Apis mellifera) globally. There is a scarcity in experimental approaches addressing this issue by using computed tomography. Our purpose was to determine the decrease in the volume, surface, density and weight alteration triggered by V. destructor in pre-imaginal stages of A. mellifera using computer-based 3-D reconstruction technology. Computer tomography used in the current study proved to be sufficiently sensitive for the detection of V. destructor in comb cells. This non-invasive experimental setup revealed that the developing worker pupae have retained their volume and surface values, whereas the weight loss triggered by V. destructor was statistically confirmed. In addition, the disappearance of lower radiodensity tissues is shown to be due to mite parasitism, which can be primarily explained by haemolymph losses. [ABSTRACT FROM AUTHOR]

Purpose: Many patients receive magnetic resonance (MR) and computed tomography (CT) scans post-operatively to review screw placement. Traditionally, CT is diagnostic but as metal artefact reduction sequences are advancing in MR, the necessity for both MR and CT scans is questionable. The objective is to establish the statistical agreeability of MR and CT for evaluation of adequate screw placement in posterior lumbar interbody fusion. Methods: This opportunistic retrospective study of 58 patients investigated 297 images of 296 implanted screws. Post-operative MR and CT images were scrutinised for depiction of lumbar pedicle screw position using a 5-point scale. Kappa value for statistical agreeability tested MR against CT. Results: The 297 images of screws resulted in strong to near-perfect agreement between MR and CT (n = 297 k = 0.8042 p < 0.025). MRI resulted in high sensitivity (88.7%) and positive predictive value (78.3%). MRI demonstrated very high specificity (96.2%) and negative predictive value (98.2%). MR depicted screws (mean 12.6 mm diameter, mean 65.3 mm length) with 50% error in diameter and 30% in length from susceptibility artefact compared to manufacturer dimensions (6.5–7.5 mm diameter, 40–50 mm length). Adequate screw placement was high despite this (85.8%). On MR, the cortex border visibility was 60.7% and the spinal canal visibility was 74.6%. Conclusion: There is strong to near-perfect agreement between MR and CT for evaluating adequate screw position in PLIF surgery. MR alone is useful for analyzing screw placement and should be considered first-line imaging in uncomplicated cases with CT analysis reserved for cases of uncertainty. Level of evidence: Level III retrospective cohort study. [ABSTRACT FROM AUTHOR]