Computed Tomography Image Analysis Research Articles

Objective analysis of orbital rim fracture CT images using curve and area measurement.

The orbital bone presents a closed curve, and fracture results in disfigurement. An image analysis procedure was developed to examine before and after corrective surgery. An ellipse and circumscribed contour embodied the closed curve. Three-dimensional (3D) computed tomography (CT) images were collected from 25 patients. Orbital rim data were generated, and binary images were created to facilitate closed curve analysis. Various indices, including the solidity value (closed curve area/convex hull area) and ellipse distance (discrepancy between the closed curve and the ellipse traversing the curve), were utilized. The ratios of various indices-including the number of vertices, solidity value, and ellipse distance-between the affected and unaffected sides showed postoperative values that were closer to 1, which would indicate perfect symmetry, than the preoperative measurements (P < 0.05). The solidity value increased, while both the ellipse distance and curvature values decreased, reflecting the transformation of bends into smooth contours following reduction surgery (P < 0.05). Significant correlations were observed between 1-solidity, ellipse distance, and curvature using the Pearson correlation test (P < 0.05). This study validated postoperative changes in various indices and established correlations among multiple values, specifically solidity, ellipse distance, and curvature. Employing multiple indices with mutual complements has provided objective information confidently.

Diagnosis of Respiratory Sarcopenia for Stratifying Postoperative Risk in Non–Small Cell Lung Cancer

Physical biomarkers for stratifying patients with lung cancer into subtypes suggestive of outcomes are underexplored. To investigate the clinical utility of respiratory sarcopenia for optimizing postoperative risk stratification in patients with non-small cell lung cancer (NSCLC). This retrospective cohort study reviewed consecutive patients undergoing lobectomy and mediastinal lymph node dissection for NSCLC at 2 institutions in Tokyo, Japan, between 2009 and 2018. Eligible patients underwent electronic computed tomography image analysis. Follow-up began at the date of surgery and continued until death, the last contact, or March 2022. Data analysis was performed from April 2022 to March 2023. Respiratory sarcopenia was identified by poor respiratory strength (peak expiratory flow rate) and was confirmed by a low pectoralis muscle index (PMI; pectoralis muscle area/body mass index). Patients with poor peak expiratory flow rate but normal PMI received a diagnosis of pre-respiratory sarcopenia. Short-term and long-term postoperative outcomes were compared among patients with a normal status, pre-respiratory sarcopenia, and respiratory sarcopenia. Group differences were analyzed using the Kruskal-Wallis test and Pearson χ2 test for continuous and categorical data, respectively. Survival differences were compared using the log-rank test. Univariable and multivariable analyses were conducted using the Cox proportional hazards model. Of a total of 1016 patients, 806 (497 men [61.7%]; median [IQR] age, 69 [64-76] years) were eligible for electronic computed tomography image analysis. The median (IQR) duration of follow-up for survival was 5.2 (3.6-6.4) years. Respiratory strength was more closely correlated with PMI than pectoralis muscle radiodensity (Pearson r2, 0.58 vs 0.29). Respiratory strength and PMI declined with aging simultaneously (both P for trend < .001). Pre-respiratory sarcopenia was present in 177 patients (22.0%), and respiratory sarcopenia was present in 130 patients (16.1%). The risk of postoperative complications escalated from 82 patients (16.4%) with normal status to 39 patients (22.0%) with pre-respiratory sarcopenia to 39 patients (30.0%) with respiratory sarcopenia (P for trend < .001), as did the risk of delayed recovery after surgery (P for trend < .001). Compared with patients with normal status or pre-respiratory sarcopenia, patients with respiratory sarcopenia exhibited worse 5-year overall survival (438 patients [87.2%] vs 133 patients [72.9%] vs 85 patients [62.5%]; P for trend < .001). Multivariable analysis identified respiratory sarcopenia as a factor independently associated with increased risk of mortality (hazard ratio, 1.83; 95% CI, 1.15-2.89; P = .01) after adjustment for sex, age, smoking status, performance status, chronic heart disease, forced expiratory volume in 1 second, diffusing capacity for carbon monoxide, C-reactive protein, albumin, carcinoembryonic antigen, histology, and pathologic stage. This study identified individuals at higher risk of poor outcomes by screening and staging respiratory sarcopenia. The early diagnosis of respiratory sarcopenia could optimize management strategies and facilitate longitudinal care in patients with NSCLC.

Kidney Disease Classification

Kidney diseases, such as tumors, cysts, and stones, are serious health conditions that affect millions globally. Medical imaging, particularly Computed-Tomography (CT) scans, plays an important role in diagnosing these conditions. Although, manual analysis of CT images by radiologist can be tiresome, fallible, and affected by human variability. This paper introduces a deep learning approach that employs Convolutional Neural Networks (CNN) for the automated classification of kidney CT scan images into four distinct categories : normal, cysts, tumors, and stones. The system is designed to boost diagnostic accuracy, reduce human error, and expedite clinical decision making. Through data preprocessing, CNN architecture design, training, and evaluation, the proposed model achieved a classification accuracy of 92%. This study highlights the significance of CNNs in medical image analysis anddtheir implicit for real- time deployment in clinical surroundings. Additionally, we compare our CNN-based approach with other advanced architectures like Vision Transformers, demonstrating CNNs' robustness and efficiency in this domain.

CT-based sarcopenia assessment: Predicting outcomes in acute infection patients.

In this retrospective cohort study, we investigated the prognostic value of sarcopenia evaluated by Computed Tomography (CT)-based indices for adverse hospitalization outcomes in patients with acute infections. We analyzed data from 225 patients admitted to the hospital for acute infections between 2019 and 2020. Patients who had undergone an abdominal CT scan either up to 1 month before or within the first 3 days of hospitalization were included. CT image analysis was used to evaluate skeletal muscle mass (by skeletal muscle index (SMI)) and muscle quality (by psoas muscle density, pMD). Low pMD was associated with higher in-hospital mortality (31% vs 11.4% p < 0.001) as well as higher longer-term mortality rates (p = 0.008 for 30 days and <0.001 for 90- and 1-year mortality). Low pMD remained an independent poor prognostic factor after controlling for confounders, with an adjusted odds ratio (aOR) of 2.74, (95% CI 1.33-5.67, p = 0.006) for 1-year mortality, and aOR of 2.61, (95% CI 1.23-5.55) for a prolonged hospital stay. Low SMI was associated with adverse outcomes, although this association was not independent after controlling for confounders. Notably, patients with both low SMI and pMD exhibited the poorest hospitalization outcomes: aOR for 1-year mortality 5.015 (95% CI 1.767-14.23, p = 0.002), and prolonged length of stay aOR 3.197, (95% CI 1.159-8.821, p = 0.025). CT-based muscle indices serve as independent prognostic factors in medical patients admitted with acute infection. Incorporating radiological assessments of sarcopenia into routine care for hospitalized patients with acute infection may enable risk stratification and early intervention in reversible conditions.

ASSOCIATIONS BETWEEN AORTIC DIAMETER AND ANTHROPOMETRIC MEASURES IN MEN: A CT STUDY

Cardiovascular diseases are a leading cause of death globally, with Ukraine experiencing a mortality rate of 64.3% in 2019, predominantly affecting men. Aortic aneurysms, assessed by aorta diameter, are significant among these diseases. While computed tomography (CT) is the gold standard for diagnosing aortic syndromes, there's a lack of comprehensive data on aortic morphometrics in Ukraine, particularly in men. This research aims to bridge this gap by analyzing CT-derived data on the ascending aorta in Ukrainian men. Aim. To enhance diagnostic accuracy and guide treatment strategies, including surgical interventions, by understanding the correlation between aortic morphology, age, and anthropometric variables in this high-risk demographic. Materials and methods. The study used DICOM images from computed tomography angiography of the aorta with contrast enhancement and ECG synchronization. Methods included anthropometric assessments, CT imaging, and statistical analysis. The study involved 62 male subjects without heart or ascending aorta pathology. Data included age, body length, and weight, which were used to calculate BMI and BSA. Diameter measurements of the aorta were taken at specific levels: the aortic ring, aortic sinus, sinotubular junction, maximum diameter of the ascending aorta, mid-section of the ascending aorta, and just before the brachiocephalic trunk departure. Statistical analyses employed the Student's t-test and Pearson's correlation analysis, with a significance level of p&lt;0.05. Results. The study encompassed 62 male subjects without cardiovascular pathology, with an average age of 52.56±13.52 years, average body length of 1.76±0.07 m, average body weight of 84.74±14.14 kg, average BMI of 27.54± 4.74 kg/m², and BSA of 2.03±0.18 m². A moderate positive correlation between age and aortic diameter was observed, ranging from +0.35 for the average diameter of the aorta at the aortic ring level to +0.57 for the maximum diameter of the aorta (p&lt;0.05). However, no significant correlations were found between aortic diameters and body height in men (p&gt;0.05). Notably, a moderate positive correlation was observed between body weight and aortic diameter at the sinotubular junction level (r=+0.61, p&lt;0.001) and the aortic sinus level (r=+0.34, p= 0.027). Furthermore, an increase in aortic diameter at the sinotubular junction level was significantly correlated with an increase in BMI (r=+0.59, p&lt;0.001) and BSA (r=+0.57, p&lt;0.001). Additionally, body surface area demonstrated a significant positive correlation with an aortic diameter at the aortic sinus level, exhibiting a moderate positive correlation (r=+0.35, р=0.023). Men with a BMI of 25 kg/m² or higher (n=45) showed significantly larger aortic diameters at almost all levels (excluding the aortic ring diameter) compared to those with lower BMIs (р&lt;0.05). Conclusions. CT-morphometric indicators of the diameter of the aorta in men normally increase with age. Body weight is directly correlated with the diameter of the aorta at the level of the sinotubular junction and aortic sinuses. In men with excess body weight and obesity, larger values of the diameters of the aorta were established at all studied levels, except for the level of the sinotubular junction.

Uncommon presentation of Castleman disease in the duodenum: a case description and computed tomography imaging analysis.

Uncommon presentation of Castleman disease in the duodenum: a case description and computed tomography imaging analysis.

RDAG U-Net: An Advanced AI Model for Efficient and Accurate CT Scan Analysis of SARS-CoV-2 Pneumonia Lesions.

Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools.

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.

Auxiliary 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. This 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. A 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. A total of 4718 and 16290 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%. The 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.