Computed Tomography Image Analysis Research Articles

Evaluation of Biocomposite Cements for Bone Defect Repair in Rat Models

Repairing or reconstructing significant bone defects is typically challenging. In the present study, two composite cements were used as scaffolds in a sub-critical femoral defect in rats. A control group and two experimental batches were used to compare the outcomes. This research aimed to investigate the osteogenic potential and toxicological tolerance of the bioproducts through histopathology and computed tomography imaging analysis at 14, 28, 56, and 90 days post-implantation. The biomaterials used in the investigation consisted of a 65% bioactive salinized inorganic filler and a 25% weight organic matrix. The organic part of the biomaterial was composed of Bis-GMA (bisphenol A-glycidyl methacrylate), UDMA (urethane dimethacrylate), HEMA (2-Hydroxyethyl methacrylate), and TEGDMA (triethylene glycol dimethacrylate), while the inorganic filler was composed of silica, barium glass, hydroxyapatite, and fluor aluminosilicate glass. The first findings of this research are encouraging, revealing that there is a slight difference between the groups treated with biomaterials, but it might be an effective approach for managing bone abnormalities. Material C1 exhibited a faster bone defect healing time compared to material C2, where bone fractures occurred in some individuals. It is unclear if the fractures were caused by the presence of the biomaterial C2 or whether additional variables were to blame. By the end of the research, the mice appeared to tolerate the biomaterials without exhibiting any inflammatory or rejection responses.

Development of fluid pathways and associated diagenetic variations in a natural CO2 leaking fault zone.

Abstract Permeability within fault zones can vary through time due to repeated deformation events and rock–fluid interactions. Understanding the history of fault zone alteration is critical when building hydrogeologic models and evaluating the risk of mechanical rock failure during subsurface storage. Newly acquired drill core recovered within the fault damage zone and fault core of the Little Grand Wash fault (LGWF) is combined with observations from outcrop, optical petrography, computerized tomography image analysis, and ultrasonic velocity measurements to characterize the rock types and preserved structural deformation features within this fault zone. These data are used to understand the history of mechanical rock failure and mineralization associated with this fluid-charged fault system. We identify multiple structural features and use their cross-cutting relationships to understand the history of deformation and their association with changes in fault zone permeability and rock mechanical properties. At the LGWF zone, structural deformation features vary temporally and are used to recognize a decoupling between fault slip and fluid flow. The formation of most open-mode veins occurred after shear failure within the LGWF zone. Early-developed shear bands are cut by carbonate veins, that are in turn cut by shear fractures, followed by a second phase of vein formation and ultimately by folding in the fault core. This sequence of formation reflects changes in mechanical rock properties due to subsurface rock–fluid interactions and indicates that the alteration of rock matrix by secondary carbonate cement results in increased unconfined compressive strength, decreased permeability, and increased ultrasonic velocity. In this fault zone, and possibly other fault zones, the changes in rock properties associated with deformation can be detected through their geophysical response.

Impact of Element Size on Rebar-Concrete Interface Microstructure Using X-ray Computed Tomography.

This paper investigates the impact of element size on the microstructure of the steel-concrete interface in self-compacting concrete (SCC). Experiments were conducted on two types of test elements: a deep beam measuring 1440 × 640 × 160 mm and a wall element measuring 2240 × 1600 × 160 mm. The SCC mix was consistently pumped from the top, using a single casting point located near the formwork's edge. Horizontal steel ribbed rebars with a diameter of 16 mm were embedded in these elements. X-ray computed tomography (CT) was employed to provide three-dimensional insights into the microstructure of the rebar-to-concrete interface. An analysis of X-ray CT images from core samples revealed that the microstructure of this interface is influenced by the distance of the specimen from the mix casting point and its vertical position within the element. The combined effects of bleeding, air-pore entrapment, and plastic settlement within the SCI were observed under the top rebars. Their extent was independent of the type of element analyzed, suggesting that the deterioration of the SCI is related to the distance from the top surface of the element. These results elucidate phenomena occurring during the fresh state of concrete near reinforcing bars and their implications for bond properties. To date, some of the standards differentiate between bond conditions according to the depth of concrete beneath the rebar. In the view of the studies, this approach may be unduly rigorous. The findings offer valuable guidance for reinforced concrete execution and design.

CT imaging analysis differentiating papillary renal neoplasm with reverse polarity from papillary renal cell carcinoma: combined with a radiomics model.

To assess the computed tomography (CT) findings of papillary renal neoplasm with reverse polarity (PRNRP) and develop a radiomics-based model to distinguish PRNRPs from papillary renal cell carcinomas (PRCCs). We analyzed 31 PRNRPs and 68 PRCCs using preoperative kidney CT. We evaluated CT features that could discriminate PRNRPs from PRCCs. A radiomics signature was constructed using features selected through a least absolute shrinkage and selection operator algorithm. A radiomics-based model incorporating a radiomics signature and subjective CT parameters using multivariate logistic regression was developed. The diagnostic performance of the CT parameters, radiomics model, and their combination was evaluated using the area under the curve (AUC). Most of PRNRPs had a round shape (93.5%), well-defined margin (100%), and persistent enhancement (77.4%). Compared with PRCC, PRNRPs exhibited distinct CT features including small size (16.7 vs. 37.7mm, P < 0.001), heterogeneity (64.5 vs. 32.4%, P = 0.004), enhancing dot sign (16.1 vs. 1.5%, P = 0.001), and high attenuation in pre-contrast CT (44.2 vs. 35.5 HU, P = 0.003). Multivariate analysis revealed smaller mass size (odds ratio [OR]: 0.9; 95% confidence interval [CI] 0.9-1.0, P = 0.013), heterogeneity (OR: 8.8; 95% CI 1.9-41.4, P = 0.006), and higher attenuation in pre-contrast CT (OR: 1.1; 95% CI 1.0-1.2, P = 0.011) as significant independent factors for identifying PRNRPs. The diagnostic performance of the combination model was excellent (AUC: 0.923). Smaller tumor size, heterogeneity, and higher attenuation in pre-contrast CT were more closely associated with PRNRPs than with PRCCs. Though the retrospective design, small sample size, and single-center data of this study may affect the generalizability of the findings, combining subjective CT features with a radiomics model is beneficial for distinguishing PRNRPs from PRCCs.

Epidemiological analysis of pulmonary artery dilation prevalence in Moscow: automated computed tomography image analysis

Epidemiological analysis of pulmonary artery dilation prevalence in Moscow: automated computed tomography image analysis

A Comprehensive Cone Beam Computed Tomography Imaging Analysis of Contralateral Mandibular First Premolars Root Canal Anatomy

A Comprehensive Cone Beam Computed Tomography Imaging Analysis of Contralateral Mandibular First Premolars Root Canal Anatomy

Computed tomography imaging analysis of a fused filament fabrication (FFF) 3D printed neck-thyroid phantom for multidisciplinary purposes

The application of the 3D printing technique for the development of low-cost phantoms is being investigated recently and requires a complex study of the interaction of printed materials with different types and qualities of radiation, as well as the characterization of printing filaments to correctly simulate human tissue attenuation. This study aims to present the Computed Tomography (CT) Imaging analysis of a fused filament fabrication (FFF) 3D printed anthropomorphic neck-thyroid phantom. The commercial phantom ATOM MAX 711 from CIRS was used as anatomy of reference for the 3D modeling base of the neck-thyroid phantom. Commercially available PLA and ABS XCT-A validated at IPEN were used in the 3D printing process in order to simulate soft and bone tissues respectively. The printing process was done using the RAISE3D PRO 2 FFF printer from IPEN. The imaging study of the phantom was performed through the analysis of images from a CT acquisition, comparing the Hounsfield Units (HU) numbers of the tissues between both CIRS and 3D printed phantoms. The developed phantom is a feasible alternative and presents some desirable characteristics for applications in radiation protection, measurements of radioisotopes incorporated in the thyroid (both contamination counters and nuclear medicine detectors) and training of techniques of acquisition of images with X rays.

Damage evolution in asphalt mixtures based on in-situ CT scanning

A novel approach utilizing in-situ Computed Tomography (CT) scanning and digital image processing techniques was developed to overcome the limitations of traditional CT image analysis in studying microcrack evolution in asphalt mixtures. Two types of graded asphalt mixtures (continuous gradation/AC and intermittent gradation/SMA) were scanned in-situ to capture internal structural changes. Digital image processing distinguished primary voids from cracks and extracted crack evolution. Multivariate regression analysis linked crack parameters to compressive strain. The study found that crack evolution during uniaxial compression in asphalt mixtures can be divided into densification, linear elastic deformation, and nonlinear failure stages. As load increases, the number, volume, and void percentage occupied by cracks rise, peak, and then decrease due to merging and engulfing after the ultimate load. The skeletal structure of cracks was obtained, and a damage evolution equation was established, linking crack parameters to damage evolution. Crack tortuosity was used to characterize the resistance effect of different gradations on crack propagation. This methodology provides new insights and technical support for studying damage evolution and understanding the failure mechanisms of asphalt mixtures.

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy

Background and purposeSymptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP. Materials and methodsAIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT’s quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort. ResultsOverall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively. ConclusionAIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

Computer‐aided diagnosis of cystic lung diseases using CT scans and deep learning

AbstractBackgroundAuxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restricting their applicability in regions with less developed medical resources. Thus, how to realize the computer‐aided diagnosis of CLDs is of great clinical value.PurposeThis work proposed a deep learning‐based method for automatically segmenting the lung parenchyma in computed tomography (CT) slice images and accurately diagnosing the CLDs using CT scans.MethodsA two‐stage deep learning method was proposed for the automatic classification of normal cases and five different CLDs using CT scans. Lung parenchyma segmentation is the foundation of CT image analysis and auxiliary diagnosis. To meet the requirements of different sizes of the lung parenchyma, an adaptive region‐growing and improved U‐Net model was employed for mask acquisition and automatic segmentation. The former was achieved by a self‐designed adaptive seed point selection method based on similarity measurement, and the latter introduced multiscale input and multichannel output into the original U‐Net model and effectively achieved the lightweight design by adjusting the structure and parameters. After that, the middle 30 consecutive CT slice images of each sample were segmented to obtain lung parenchyma, which was employed for training and testing the proposed multichannel parallel input recursive MLP‐Mixer network (MPIRMNet) model, achieving the computer‐aided diagnosis of CLDs.ResultsA total of 4718 and 16 290 CT slice images collected from 543 patients were employed to validate the proposed segmentation and classification methods, respectively. Experimental results showed that the improved U‐Net model can accurately segment the lung parenchyma in CT slice images, with the Dice, precision, volumetric overlap error, and relative volume difference of 0.96 ± 0.01, 0.93 ± 0.04, 0.05 ± 0.02, and 0.05 ± 0.03, respectively. Meanwhile, the proposed MPIRMNet model achieved appreciable classification effect for normal cases and different CLDs, with the accuracy, sensitivity, specificity, and F1 score of 0.8823 ± 0.0324, 0.8897 ± 0.0325, 0.9746 ± 0.0078, and 0.8831 ± 0.0334, respectively. Compared with classical machine learning and convolutional neural networks‐based methods for this task, the proposed classification method had a preferable performance, with a significant improvement of accuracy of 10.74%.ConclusionsThe work introduced a two‐stage deep learning method, which can achieve the segmentation of lung parenchyma and the classification of CLDs. Compared to previous diagnostic tasks targeting single CLD, this work can achieve various CLDs’ diagnosis in the early stage, thereby achieving targeted treatment and increasing the potential and value of clinical applications.

Predictors of neuromodulation during single-shot thermal atrial fibrillation ablation: a computed tomography imaging analysis

Abstract Background Ganglionated plexi (GPs) are part of the cardiac autonomic nervous system (CANS), which play an important role in the induction and maintenance of atrial fibrillation (AF). Because of the anatomic proximity of the pulmonary veins with GPs, pulmonary vein isolation (PVI) using thermal (radiofrequency (RF) or cryo) ablation, has been shown to concomitantly affect the GPs, as suggested by increased heart rate (HR) after ablation. Right superior pulmonary vein (RSPV) isolation has been reported to have the most evident inhibitory responses to the CANS via modulation of the anterior right ganglionated plexus. Nonetheless, there are also cases with little to no effect on neuromodulation post-ablation. Purpose To assess the clinical and anatomical predictors of thermal ablation-induced CANS changes, as assessed via variations in resting HR post-ablation. Methods We retrospectively analyzed consecutive patients with symptomatic paroxysmal AF who underwent first-time PVI by cryoballoon (CB) or RF balloon (RFB) between January 2022 to November 2022. Patients who were not in sinus rhythm, had cardiac implantable electronic devices, or did not have a contrast-enhanced computed tomography before the ablation were excluded. Pre-ablation and 24-hour post-ablation ECGs in sinus rhythm were collected and analyzed to assess HR. Results A total of 83 patients (CB group, n = 43 vs RFB group, n = 40) were included in the analysis. Baseline patients’ characteristics were similar between the two groups (Table 1). A significant HR increase (ΔHR ≥ 15 bpm) at 24h post-ablation occurred in 31 patients [CB group 16 (52 %), RFB group 15 (48 %)]. Predictors of increased HR were the distance between superior vena cava (SVC) and RSPV (OR 0.51, CI 0.35 - 0.75, p value &amp;lt; 0.01) and age (OR 0.94, CI 0.89 - 0.99, p value &amp;lt; 0.01) (Table 2). Conclusions Single-shot thermal PVI influences the CANS, especially in patients with a shorter SVC - RSPV distance and in younger patients.Table 1Table 2

The texture analysis of computed tomography studies in clear cell renal cell carcinoma: reproducibility of 2D and 3D segmentation

Background: Differentiation of tumor grade at the preoperative stage is of utmost importance for the modification of the treatment strategy and the extent of operation. However, the routine analysis of computed tomography (CT) data in clear cell renal cell carcinoma (ccRCC) does not allow for reliable determination of the tumor grade.&#x0D; Aim: To assess the reproducibility of the results of 2D and 3D segmentation of a kidney tumor in the cortico-medullary and nephrographic phases of CT studies, as well as the reproducibility of the first order texture parameters for 2D and 3D tumor segmentation in patients with verified ccRCC.&#x0D; Materials and methods: This retrospective study included the CT data of 50 patients with morphologically verified ccRCC obtained before their surgical treatment. The first patient group included the patients with the renal tumor size in the axial plane of ≥ 4 cm (28 patients, 29 CT studies), and the second patient group included those with the renal tumor size in axial plane of 4 cm (22 patients, 23 CT studies). Two radiologists independently performed segmentation of the renal tumor in the cortico-medullary and nephrographic phases of CT procedures done under a standard protocol with the bolus intravenous contrast enhancement. A two-dimensional region of interest (2D ROI) was selected by the investigators on a subjectively selected axial slice, where the tumor had the largest size. When forming a three-dimensional region of interest (3D ROI), the entire tumor volume was segmented. Next, the statistical analysis of the segmentation results and the results of calculation of the first order texture indices was performed with calculation of the intra-class correlation coefficient (ICC) to assess the strength of the data correlation. The ICC of ≥ 0.75 demonstrated the reproducibility of the segmentation results and the first order texture indices.&#x0D; Results: The 3D segmentation method for ccRCC demonstrated the best ROI reproducibility results, regardless of the tumor size and the phase of contrast enhancement, with the ICC values of 0.961 (95% confidence interval: 0.946–0.971) for the cortico-medullary phase and 0.969 (95% CI: 0.958–0.977) for the nephrographic phase. The 2D tumor segmentation method showed unsatisfactory ROI reproducibility, with the ICC values of ≤ 0.058; however, the unsatisfactory reproducibility of the segmentation results in the patients with ccRCC tumor size of ≥ 4 cm did not significantly affect the reproducibility of the Entropy and Energy texture indices (good to excellent correlation). With the 3D segmentation of ccRCC, most first-order texture metrics were reproducible, with the exception of the Kurtosis parameter. The Entropy and Energy scores in both patient groups demonstrated a high degree of reproducibility. In the 2D tumor segmentation, high reproducibility of the first order texture metrics was obtained for the Entropy and Energy indices.&#x0D; Conclusion: The 3D segmentation of the CT data for ccRCC has high reproducibility, the most first-order textural features were excellently reproducible when segmentations were performed in 3D. The 2D CT data segmentation method for ccRCC demonstrated low reproducibility; however, some of the first order texture indices were reproducible. Both segmentation methods can be used for the texture analysis of CT images.

Preferential flow of phosphorus and nitrogen under steady‐state saturated conditions

AbstractRepeated broiler litter application on agricultural lands can cause nutrient enrichment of subsurface effluent, especially with the existence of preferential flow through soil macropores. Previous studies quantifying soil macropores have not attempted to establish a connection of soil macropore characteristics with the subsurface nutrient (nitrogen [N] and phosphorus [P]) losses, across different topographical locations in the field. This study investigated the effect of broiler litter application and preferential flow on subsurface nutrient transport (N and P) at different topographical positions (upslope, midslope, and downslope) in a no‐till pasture field located in Alabama, USA. Twelve intact soil columns (150 mm id and 500 mm length) were used, and the nutrient leaching measurements from laboratory experiments were linked to soil macropore characteristics quantified using X‐ray computed tomography image analysis and solute transport modeling. Treatments included surface broadcast broiler litter (5 Mg ha−1, on dry basis) and unamended control. Leachates were analyzed for dissolved reactive P (DRP), total P (TP), and nitrate + nitrite‐N (NO3− + NO2−–N). The bromide breakthrough curves provided evidence of preferential flow in all columns. Litter application significantly increased leachate P concentrations, and average TP and DRP concentrations were significantly higher in the leachate from upslope columns compared to those at downslope location. The NO3−–N concentrations in leachate exceeded the US EPA drinking water standard of 10 mg L−1 in all the treatment columns. The highest flow‐weighted mean concentrations of TP and DRP, at 2.7 and 2.5 mg L−1, respectively, were recorded in the upslope columns. Soil physicochemical properties and nutrient leaching losses varied substantially across topographical positions, indicating a need for variable litter application rates to reduce P build‐up and subsequent leaching in vulnerable locations within the field. The relevance of the effect of topographic position on nutrient leaching found in this study should be further tested by investigating a wider range of slopes and soil types in pastures.

A New Methodology for Evaluation of Large Vestibular Aqueduct in CT and MRI Images.

Development of a new method for large vestibular aqueduct (LVA)/large endolymphatic sac anomaly (LESA) assessment using magnetic resonance imaging (MRI) and computed tomography (CT)/cone beam CT (CBCT) images. The secondary objective was to compare both modalities. The gold standard for LVA diagnosis is the analysis of CT images using Valvassori and Clemis or Cincinnati criteria. The previous studies showed inconclusive results regarding the correlation between audiological and radiological data. Retrospective analysis of radiological images from 173 patients (315 ears), who were diagnosed with LVA/LESA based on CT/CBCT and/or MRI images of the temporal bone. The images obtained using both techniques were used to measure the following dimensions of vestibular aqueduct (VA)/endolymphatic duct (ED)/intraosseous endolymphatic sac (ES): width of the opening, length, and width at external aperture. In MRI images, the maximal contact diameters of the extraosseous or intraosseous ES and dura mater were measured as well. LVA has been reported to be bilateral in 82% (142 patients) and unilateral in 18% (31 patients) of cases. Comparison of MRI and CT/CBCT measurements showed a moderate correlation (0.64) in external aperture, a moderate correlation (0.57) in the width of the VA opening, and a weak correlation (0.34) in length measurements (p < 0.05). We developed a new method to identify the heterogeneous pathology of LVA/LESA using reconstruction along the VA/ED/intraosseous ES axis, three measurements on two planes, and focus on the maximal contact diameter between the extraosseous or intraosseous ES and dura mater.

A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis

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.

Classificação textural de rochas carbonáticas do pré-sal da Bacia de Santos e análise do sistema permoporoso: uma abordagem baseada na interpretação de imagens de tomografia computadorizada de raios X

Pre-salt lacustrine carbonates remain in evidence for being rocks of high complexity and responsible for the great economic potential of the Santos Basin. Aiming to contribute to studies in this field, the objective of this research was to define the key textures of the carbonate rocks in X-ray computed tomography (CT) images. For the execution of this study, core samples from two wells were used, and the adopted methodology comprises detailed geological description, characterization of the permoporous system, definition of the main textures in CT images, and analysis of the relationship between textures and their permoporous systems. The description of the main properties enabled the definition of six lithofacies and, through the analysis of CT images, a recurring pattern of certain textural attributes in specific lithofacies was identified. Thus, a representative proposal with four textural classes was established: laminated, shrubby, clastiform fragmented, and recrystallized. A direct relationship was observed between certain textures and the progression of diagenetic processes, as well as their corresponding permoporous systems. Finally, the proposed textural classification of CT images applied to Brazilian pre-salt lacustrine carbonate rocks demonstrates effectiveness in standard segmentation performed by expert technicians, enabling rapid identification of reservoir intervals of interest. For future work, it is recommended to employ this classification as an input base for training algorithms using machine learning techniques.

Cochlear implant positioning: development and validation of an automatic method using computed tomography image analysis.

An in-house designed semi-automatic algorithm was developed to analyse a 3D model of the skull. The feasibility of drilling the recess was determined by a gradient descent method to search for the thickest part of the temporal bone. Feasibility was determined by the residual bone thickness which was calculated after a simulated drilling of the recess at the thickest position. An initial validation of the algorithm was performed by measuring the accuracy of the algorithm on five 3D models with known thickest locations for the recess. The accuracy was determined by a part comparison between the known position and algorithm provided position. In four of the five validation models a standard deviation for accuracy below the predetermined cut-off value of 4.2 mm was achieved between the actual thickest position and the position determined by the algorithm. Furthermore, the residual thickness calculated by the algorithm showed a high agreement (max. 0.02 mm difference) with the actual thickness. With the developed algorithm, a semi-automatic method was created to analyse the temporal bone thickness within a specified region of interest on the skull. Thereby, providing indications for surgical feasibility, potential risks for anatomical structures and impact on procedure time of cochlear implantation. This method could be a valuable research tool to objectively assess feasibility of drilling a recess in patients with thin temporal bones preoperatively.

An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of 97.72%pm 1.57, a sensitivity of 97.76%pm 4.08, and a specificity of 98.87%pm 2.09, indicating a high level of prediction accuracy.

4D evolutions of cracks, voids, and corrosion products in reinforced concrete materials

This research paper presents a comprehensive investigation into the corrosion process in reinforced concrete structures using advanced analytical techniques, namely non-destructive X-ray computed tomography (CT) imaging, scanning electron microscopy (SEM) images, energy dispersive x-ray spectrometry (EDS), and Raman spectroscopy. The CT image analysis allowed for the identification and quantification of pore structures, crack propagation, and corrosion progression at different stages of corrosion. CT scanning and data analysis offer valuable 4D (3D spatial + time) insights into corrosion in reinforced concrete, revealing changes in pore sizes, with smaller pores increasing and larger pores decreasing as corrosion progresses. Our investigation reveals dynamic changes in reinforced concrete pores during the accelerated corrosion test leading to new pore formation and cracking. The research identifies two distinct types of cracks: one filled with corrosion products and the other, zipper-like cracks, resulting from the connection of deformed pores without corrosion products. The SEM images and EDS analysis confirmed the absence of corrosion products within these unique zipper cracks, suggesting a different mechanism of crack formation compared to the first type of cracks. The results revealed two distinct categories of corrosion products: iron oxides and iron hydroxides, with their distribution correlated to the duration of accelerated corrosion testing. The integration and verification of results from X-ray CT imaging and Raman spectroscopy established a comprehensive understanding of corrosion-induced damage in the reinforced concrete specimen, shedding light on complex interactions among different corrosion products during the corrosion process. These findings offer crucial insights for better understanding of the corrosion process in reinforced concrete paving the way for future development of effective treatments and strategies to mitigate corrosion impact.

Explanatory sequential mixed-methods approach to understand how registered dietitians implemented computed tomography skeletal muscle assessments in clinical practice.

There is a need to adopt valid techniques to assess skeletal muscle (SM) in clinical practice. SM can be precisely quantified from computed tomography (CT) images. This study describes how registered dietitians (RDs), trained to quantify SM from CT images, implemented this technique in clinical practice. This was an explanatory sequential mixed-methods design with a quantitative and a qualitative phase. RDs collected data describing how they implemented CT SM assessments in clinical practice, followed by a focus group exploring barriers and enablers to using CT SM assessments. RDs (N = 4) completed 96 CT SM assessments, with most (94%, N = 90/96) taking <15 min to complete. RDs identified reduced muscle mass in 63% (N = 45/72) of men and 71% (N = 17/24) of women. RDs used results of CT SM assessments to increase protein composition of the diet/nutrition support, advocate for initiation or longer duration of nutrition support, coordinate nutrition care, and provide nutrition education to patients and other health service providers. The main barriers to implementing CT SM assessments in clinical practice related to cumbersome health system processes (ie, CT image acquisition) and challenges integrating CT image analysis software into the health system computing environment. Preliminary results suggest RDs found CT SM assessments positively contributed to their nutrition care practice, particularly in completing nutrition assessments and in planning, advocating for, and implementing nutrition interventions. Use of CT SM assessments in clinical practice requires innovative IT solutions and strategies to support skill development and use in clinical nutrition care.