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Objective: This study aims to develop and validate an enhanced computed tomography (CT)-based radiomics model to differentiate gastric schwannomas (GS) from gastrointestinal stromal tumors (GIST) across various risk categories. Methods: This retrospective analysis was conducted on 26 GS and 82 GIST cases, all confirmed by postoperative pathology. Data was divided into training and validation cohorts at a 7:3 ratio. We collected patient demographics, clinical presentations, and detailed CT imaging characteristics. Through univariable and multivariable logistic regression analyses, we identified independent predictors for discriminating between GS and GIST, facilitating the construction of a conventional model. Radiomic features were extracted and refined through manual 3D segmentation of venous phase thin-slice images to develop a radiomics model. Subsequently, we constructed a comprehensive combined model by integrating selected clinical and radiomics indicators. The diagnostic performances of all models in differentiating GS from GIST and stratifying GISTs according to malignancy risk were evaluated. Results: We identified several key independent variables distinguishing GS from GIST, including tumor location, cystic changes, degree of enhancement in arterial phase, and enhancement uniformity. The conventional model achieved AUCs of 0.939 and 0.869 in the training and validation cohort, respectively. Conversely, the radiomics model, predicated on eight pivotal radiomics features, demonstrated AUCs of 0.949 and 0.839. The combined model, incorporating tumor location, degree of enhancement in arterial phase, enhancement uniformity, and a radiomics model derived rad-score, significantly outperformed the traditional approach, achieving AUCs of 0.989 and 0.964 in the respective cohorts. The combined model showed superior diagnostic accuracy in distinguishing GS from GIST, as well as GS from high or low malignancy potential GISTs, as evidenced by IDI values of 0.2538, 0.2418, and 0.2749 (P<0.05 for all). Conclusion: The combined model based on CT imaging features and radiomics features presents a promising non-invasive approach for accurate preoperative differentiation between gastric schwannomas and gastrointestinal stromal tumors. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses. Recent Findings: Recent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of standing CT systems has improved the practicality and accuracy of live animal imaging, aiding early diagnosis of SCB pathologies. Summary: While imaging advancements have enhanced our understanding of SCB, further research is required to elucidate the underlying mechanisms of joint disease and articular surface failure. Combining imaging with mechanical testing, computational modelling, and artificial intelligence (AI) promises earlier detection and better management of joint disease. Future research should refine these modalities and integrate them into clinical practice to enhance joint health outcomes in veterinary and human medicine. [ABSTRACT FROM AUTHOR]

Documented radiological and physiological anomalies among coronavirus disease 2019 survivors necessitate prompt recognition of residual pulmonary parenchymal abnormalities for effective management of chronic pulmonary consequences. This study aimed to devise a predictive model to identify patients at risk of such abnormalities post-COVID-19. Our prognostic model was derived from a dual-center retrospective cohort comprising 501 hospitalized COVID-19 cases from July 2022 to March 2023. Of these, 240 patients underwent Chest CT scans three months post-infection. A predictive model was developed using stepwise regression based on the Akaike Information Criterion, incorporating clinical and laboratory parameters. The model was trained and validated on a split dataset, revealing a 33.3% incidence of pulmonary abnormalities. It achieved strong discriminatory power in the training set (area under the curve: 0.885, 95% confidence interval 0.832–0.938), with excellent calibration and decision curve analysis suggesting substantial net benefits across various threshold settings. We have successfully developed a reliable prognostic tool, complemented by a user-friendly nomogram, to estimate the probability of residual pulmonary parenchymal abnormalities three months post-COVID-19 infection. This model, demonstrating high performance, holds promise for guiding clinical interventions and improving the management of COVID-19-related pulmonary sequela. [ABSTRACT FROM AUTHOR]

Abstract Purpose The goal of this retrospective study was to perform a CT imaging assessment of thoracic pedicles to provide a representative understanding of pedicle morphology for pedicle-based fixation systems commonly used in orthopedics, trauma and neurosurgery. This study aimed to better understand the morphology of the spine and give spine surgeons a better understanding of thoracic spine anatomy. Methods In this study, we retrospectively measured the thoracic spine pedicles of a total of 16 males and 16 females, totaling in 768 individual pedicles. For the measurements, we used standardized planes in computed tomography imaging with a maximum slice thickness of 1 mm. Results In brief, we identified significant differences in various measurements of male and female pedicle morphology. The medial cortical wall of the pedicles was significantly thicker than the lateral wall, and, in both sexes, the thoracic vertebral body number four was the vertebra with the least amount of cortical bone in the pedicle. Conclusions Surgeons performing operations involving pedicle screw placement should be aware of the sex-specific differences in thoracic spine pedicle morphology noted in this research.

Xiang Wen,1,&ast; Xiaohui Wu,1,&ast; Zhishan Deng,1,&ast; Fan Wu,1,2 Huajing Yang,1 Shan Xiao,1 Cuiqiong Dai,1 Changli Yang,3 Shuqing Yu,4 Ruiting Sun,1 Pixin Ran,1,2 Yumin Zhou1 1State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease & National Center for Respiratory Medicine & Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 2Guangzhou National Laboratory, Guangzhou, Guangdong, People’s Republic of China; 3Wengyuan County People’s Hospital, Shaoguan, People’s Republic of China; 4Lianping County People’s Hospital, Heyuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yumin Zhou; Pixin Ran, Email zhouyumin410@126.com; pxran@gzhmu.edu.cnBackground: The previous findings on the correlation between spirometry and high-density lipoprotein (HDL) cholesterol are intriguing yet conflicting. The aim of this research is to evaluate the relationship between HDL levels and spirometry as well as imaging parameters in patients with chronic obstructive pulmonary disease (COPD) in China.Methods: This study encompasses a total of 907 COPD patients. Participants with complete data from questionnaire interviews, lipid profile examinations, spirometry testing, and computed tomography (CT) scans were included in the analysis. A generalized additive model was employed to identify the non-linear relationship between HDL levels and both spirometry and imaging parameters. In the presence of non-linear correlations, segmented linear regression model was applied to ascertain threshold effects.Results: After adjusting for various factors, we found a non-linear correlation between HDL levels and spirometry/imaging parameters, with an inflection point at 4.2 (66 mg/dL). When Ln (HDL) was below 4.2, each unit increase correlated significantly with reduced post-bronchodilator FEV1 (0.32L, 95% CI: 0.09– 0.55), decreased predicted FEV1% (11.0%, 95% CI: 2.7– 19.3), and lowered FEV1/FVC (8.0%, 95% CI: 4.0– 12.0), along with notable increases in Ln (LAA− 950) by 1.20 (95% CI: 0.60– 1.79) and Ln (LAA− 856) by 0.77 (95% CI: 0.37– 1.17). However, no significant associations were observed when Ln (HDL) was greater than or equal to 4.2.Conclusion: A non-linear correlation existed between HDL levels with lung function and CT imaging in COPD patients. Prior to reaching 66 mg/dL, an elevation in HDL was significantly associated with impaired lung function, more severe gas trapping and emphysema.Keywords: COPD, high-density lipoprotein, lung function, computed tomography imaging

Purpose: The goal of this retrospective study was to perform a CT imaging assessment of thoracic pedicles to provide a representative understanding of pedicle morphology for pedicle-based fixation systems commonly used in orthopedics, trauma and neurosurgery. This study aimed to better understand the morphology of the spine and give spine surgeons a better understanding of thoracic spine anatomy. Methods: In this study, we retrospectively measured the thoracic spine pedicles of a total of 16 males and 16 females, totaling in 768 individual pedicles. For the measurements, we used standardized planes in computed tomography imaging with a maximum slice thickness of 1 mm. Results: In brief, we identified significant differences in various measurements of male and female pedicle morphology. The medial cortical wall of the pedicles was significantly thicker than the lateral wall, and, in both sexes, the thoracic vertebral body number four was the vertebra with the least amount of cortical bone in the pedicle. Conclusions: Surgeons performing operations involving pedicle screw placement should be aware of the sex-specific differences in thoracic spine pedicle morphology noted in this research. [ABSTRACT FROM AUTHOR]

Background: Acromioclavicular joint (ACJ) disruption occurs frequently in athletes engaged in contact sports. However, the current understanding of ACJ biomechanics during muscle-driven functional activities and the influence of different treatment approaches (eg, reconstruction surgery vs nonoperative methods) on ACJ kinematics and stability remains limited. The absence of precise in vivo biomechanical measurement modalities for scapular and clavicular kinematics contributes significantly to this lack of understanding. Purposes/Hypothesis: The purposes of this study were to determine whether dynamic stereo x-ray (DSX) imaging can be used to evaluate the in vivo kinematics of the ACJ and to provide preliminary comparative data on ACJ kinematics, range of motion, and isometric strength of surgically reconstructed or nonoperatively treated ACJ shoulders and their uninjured contralateral shoulders. It was hypothesized that ACJ kinematics could be measured successfully using DSX and that surgically and nonoperatively treated shoulders would show abnormal 3-dimensional (3-D) ACJ kinematics compared with the uninjured contralateral. Study Design: Controlled laboratory study. Methods: In this cross-sectional study, 11 participants who had undergone unilateral ACJ reconstruction surgery and 3 patients who received nonoperative treatment were enrolled. ACJ kinematics were assessed during active forward flexion, scaption, and abduction through high-speed DSX imaging, complemented by 3-D bone models obtained via computed tomography (CT) scans. To gauge kinematic differences, a 1-dimensional statistical parametric mapping method was employed, which compared outcomes in the index limb to those in the uninjured counterpart. In addition, the range of motion and isometric strength at various abduction angles were analyzed, employing a repeated-measures analysis of variance to compare the affected and uninjured sides. Results: Leveraging a combination of DSX imaging and patient-specific CT bone models, ACJ kinematics was measured successfully during movements along anatomic planes. Preliminary findings from this investigation revealed no detectable differences between the surgically reconstructed and uninjured sides in ACJ biomechanics, shoulder range of motion, and isometric strength outcomes. However, on average, the nonoperatively treated shoulders demonstrated increased internal rotation, upward rotation, and posterior tilting of the scapula relative to the clavicle (no statistical analyses were performed due to the small sample size). Conclusion: DSX imaging is a promising tool for evaluating potential in vivo kinematic abnormalities in the ACJ during muscle-driven activities, laying the groundwork for further investigations in both ACJ-reconstructed and nonreconstructed patients. This study furnished essential data for conducting power analyses and designing future studies with an adequate sample size to investigate the impact of different treatment approaches on shoulder girdle mechanics. Clinical Relevance: With its potential for accurately characterizing shoulder girdle kinematics post-ACJ injury, DSX imaging can offer valuable insights for future clinical studies, facilitating informed decisions regarding the short- and long-term impacts of treatment choices on shoulder health and function. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, advances in deep learning have transformed the analysis of medical imaging, especially in spine oncology. Computed Tomography (CT) imaging is crucial for diagnosing, planning treatment, and monitoring spinal tumors. This review aims to comprehensively explore the current uses of deep learning tools in CT-based spinal oncology. Additionally, potential clinical applications of AI designed to address common challenges in this field will also be addressed. In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice. [ABSTRACT FROM AUTHOR]

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ObjectiveThis study aims to develop and validate an enhanced computed tomography (CT)-based radiomics model to differentiate gastric schwannomas (GS) from gastrointestinal stromal tumors (GIST) across various risk categories.MethodsThis retrospective analysis was conducted on 26 GS and 82 GIST cases, all confirmed by postoperative pathology. Data was divided into training and validation cohorts at a 7:3 ratio. We collected patient demographics, clinical presentations, and detailed CT imaging characteristics. Through univariable and multivariable logistic regression analyses, we identified independent predictors for discriminating between GS and GIST, facilitating the construction of a conventional model. Radiomic features were extracted and refined through manual 3D segmentation of venous phase thin-slice images to develop a radiomics model. Subsequently, we constructed a comprehensive combined model by integrating selected clinical and radiomics indicators. The diagnostic performances of all models in differentiating GS from GIST and stratifying GISTs according to malignancy risk were evaluated.ResultsWe identified several key independent variables distinguishing GS from GIST, including tumor location, cystic changes, degree of enhancement in arterial phase, and enhancement uniformity. The conventional model achieved AUCs of 0.939 and 0.869 in the training and validation cohort, respectively. Conversely, the radiomics model, predicated on eight pivotal radiomics features, demonstrated AUCs of 0.949 and 0.839. The combined model, incorporating tumor location, degree of enhancement in arterial phase, enhancement uniformity, and a radiomics model derived rad-score, significantly outperformed the traditional approach, achieving AUCs of 0.989 and 0.964 in the respective cohorts. The combined model showed superior diagnostic accuracy in distinguishing GS from GIST, as well as GS from high or low malignancy potential GISTs, as evidenced by IDI values of 0.2538, 0.2418, and 0.2749 (P

Abstract Objective To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis. Methods Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively. Results Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89. Conclusion It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application.

Background: The use of cuffed endotracheal tubes (ETTs) has become the standard of care in pediatric practice. The rationale for the use of a cuffed ETT is to minimize pressure around the cricoid while providing an effective airway seal. However, safe care requires that the cuff lie distal to the cricoid ring following endotracheal intubation. The current study demonstrates the capability of computed tomography (CT) imaging in identifying the position of the cuff of the ETT in intubated patients. Methods: The study included patients ranging in age from 1 month to 10 years who underwent neck and chest CT imaging that required general anesthesia and endotracheal intubation. The location of the ETT and of the cuff within the airway was determined from axial CT images at three levels (proximal, middle, and distal). Anatomical orientations were tabulated, and percent chances of each orientation were determined for the ETT and the cuff. Results: The study cohort included 42 patients ranging in age from 1 to 114 months. An ETT with a polyvinylchloride cuff was used in 24 patients, and an ETT with a polyurethane cuff was used in 18 patients. The ETT was located near the posterior wall of the trachea in approximately 24–38% of patients, being most likely to be centrally located at the proximal end and at its mid-portion. The middle part of the cuff was most likely to be positioned in the mid-portion of the trachea but tended to skew anteriorly at both the proximal and distal ends. Conclusion: This is the first study using CT imaging to identify the uniformity of cuff inflation within the trachea in children. With commonly used cuffed ETTs, cuff inflation and the final position of ETT cuff within the tracheal lumen were not uniform. Future investigations are needed to determine the reasons for this asymmetry and its clinical implications.

Aim: Pulmonary artery involvement is a severe complication of Behcet's disease (BD). Although venous thrombosis is common in BD, pulmonary embolism is considered to be rare because the inflammatory nature makes the thrombi strongly adherent to the venous walls. This study aimed to define the radiological characteristics of pulmonary artery thrombosis (PAT) on computed tomography (CT) imaging in BD patients. Methods: We retrospectively evaluated 165 BD patients with vascular involvement. Among the patients with venous involvement (n = 146), we identified 65 patients who had undergone thorax CT imaging previously. Fourteen patients who were diagnosed with PAT were included in the study. Expert radiologists re‐evaluated the patients' initial and control thorax CT scans, classified the PAT as acute or chronic based on their radiological features. Results: The patients' median age was 35 (min–max: 15–60) years at the time of the initial CT scan, and nine were male. Twelve (85.7%) patients were symptomatic at the time of CT evaluation. Upon re‐evaluating the thorax CTs, acute PAT was diagnosed in six (42.8%); chronic PAT was detected in eight (57.1%) patients. Two patients with chronic PAT also had acute PAT. Pulmonary artery aneurysms were present in three (21.4%) patients, and intracardiac thrombus was found in three (21.4%) patients. Conclusion: A significant number of BD patients with venous involvement had radiological findings consistent with acute PAT potentially due to pulmonary emboli in this study. The clinical importance of these lesions has to be defined with future studies. [ABSTRACT FROM AUTHOR]

Background: The use of cuffed endotracheal tubes (ETTs) has become the standard of care in pediatric practice. The rationale for the use of a cuffed ETT is to minimize pressure around the cricoid while providing an effective airway seal. However, safe care requires that the cuff lie distal to the cricoid ring following endotracheal intubation. The current study demonstrates the capability of computed tomography (CT) imaging in identifying the position of the cuff of the ETT in intubated patients. Methods: The study included patients ranging in age from 1 month to 10 years who underwent neck and chest CT imaging that required general anesthesia and endotracheal intubation. The location of the ETT and of the cuff within the airway was determined from axial CT images at three levels (proximal, middle, and distal). Anatomical orientations were tabulated, and percent chances of each orientation were determined for the ETT and the cuff. Results: The study cohort included 42 patients ranging in age from 1 to 114 months. An ETT with a polyvinylchloride cuff was used in 24 patients, and an ETT with a polyurethane cuff was used in 18 patients. The ETT was located near the posterior wall of the trachea in approximately 24-38% of patients, being most likely to be centrally located at the proximal end and at its mid-portion. The middle part of the cuff was most likely to be positioned in the mid-portion of the trachea but tended to skew anteriorly at both the proximal and distal ends. Conclusion: This is the first study using CT imaging to identify the uniformity of cuff inflation within the trachea in children. With commonly used cuffed ETTs, cuff inflation and the final position of ETT cuff within the tracheal lumen were not uniform. Future investigations are needed to determine the reasons for this asymmetry and its clinical implications. [ABSTRACT FROM AUTHOR]

Objective: To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis. Methods: Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively. Results: Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89. Conclusion: It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application. [ABSTRACT FROM AUTHOR]

Background Primary pericardial sarcomas are extremely rare malignancies. In this case of primary pericardial synovial sarcoma, we discuss the initial steps to work-up pericardial effusions and review features that warrant more detailed investigation. Case summary A 29-year-old male with no relevant past medical history presents with a few weeks of fatigue, dyspnoea, orthopnoea, leg swelling, and back pain. Transthoracic echocardiogram revealed pericardial effusion for which pericardiocentesis and drain placement were done. He was discharged with a diagnosis of post-viral pericarditis. He returned 5 months later with worsening symptoms. Advanced imaging with cardiac magnetic resonance imaging (CMR) showed heterogeneous pericardial mass later revealed to be a high-grade synovial sarcoma on biopsy. The patient was started on a doxorubicin-based chemotherapy regimen, but due to kidney dysfunction and multi-organ failure, he was transitioned to palliative care measures. Discussion Transthoracic echocardiogram and computed tomography are often the initial tests of choice for pericardial effusions with pericardiocentesis recommended for effusions with tamponade physiology, for moderate-to-large effusions, or if there is concern for infection/neoplasm. Due to improved tissue characterization and spatial resolution, CMR and positron emission tomography should also be considered for atypical or recurrent pericardial effusions to assess for less common aetiologies such as malignancy. [ABSTRACT FROM AUTHOR]

Lung cancer poses a significant threat, contributing significantly to cancer-related mortality. Computer-aided detection plays a pivotal role, particularly in the automated identification of pulmonary nodules, assisting radiologists in diagnosis. Despite the remarkable efficacy of deep convolutional neural networks in lesion identification, the detection of small nodules remains an enduring challenge. A conventional automated detection framework encompasses two critical stages: candidate detection and false positive reduction. This study introduces a novel approach named ReRointNet, focusing on meticulous lung nodule localization and detection through strategically placed sample points. To enhance nodule detection, we propose integrating PointNet anchors with RPN anchors. PointNet, operating on local key points, facilitates this integration. The synergy achieved by merging these anchors within our RePointNet framework enhances nodule detection rates and substantially improves localization accuracy. Post-detection, identified nodules undergo classification using the 3D Convolutional Neural Networks (CNN) method. Our contribution presents a novel paradigm for nodule detection in lung Computed Tomography (CT) images, with reduced computational costs and improved memory efficiency. The combined utilization of RePointNet and 3DCNN demonstrates proficiency in identifying nodules of various sizes, including small nodules. Our research underscores the superiority of lung nodule identification through the utilization of RePointNet based on point information, surpassing conventional networks. Rigorous evaluations of the LUNA16 dataset reveal our method's superior performance compared to state-of-the-art systems, achieving a notable sensitivity of 91.6 percent at a speed of 0.9 frames per second. These findings underscore the potential of our proposed approach in advancing precise lung nodule diagnosis, offering invaluable support to healthcare practitioners and radiologists engaged in diagnosing lung cancer patients. [ABSTRACT FROM AUTHOR]

Automated detection of anterior mediastinal nodular lesions (AMLs) has significance for clinical usage as it is challenging for radiologists to accurately identify AMLs from chest computed tomography (CT) imaging due to various factors, including poor resolution, variations in intensity and the similarity of the AMLs to other tissues. To assist radiologists in AML detection from chest CT imaging, a UNet-based computer-aided detection (CADe) system is proposed to segment AMLs from slice images of the chest CT scans. The proposed network adopts a modified UNet architecture. To guide the proposed network to selectively focus on AMLs and potentially disregard others in the image, different attention mechanisms are utilized in the proposed network, including the self-attention mechanism and the convolutional block attention module (CBAM). The proposed network was trained and evaluated on 180 chest CT scans which consist of 180 AMLs. 90 AMLs were identified as thymic cysts, and 90 AMLs were diagnosed as thymoma. The proposed network achieved an average dice similarity coefficient (DSC) of 93.23 with 5-fold cross-validation, for which the mean Intersection over Union (IoU), sensitivity and specificity were 90.29, 93.98 and 95.68 respectively. Our method demonstrated an improved segmentation performance over state-of-the-art segmentation networks, including UNet, ResUNet, TransUNet and UNet++. The proposed network employing attention mechanisms exhibited a promising result for segmenting AMLs from chest CT imaging and could be used to automate the AML detection process for achieving improved diagnostic reliability. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of −950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (−950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete. [ABSTRACT FROM AUTHOR]

The present work demonstrates the biosynthesis of silver nanoparticles (AgNPs) using Coffea arabica leaf extract. The AgNPs prepared by green route from C. arabica leaf were characterized through UV–visible, X-ray diffraction, transmission electron microscopy and energy-dispersive electron spectroscopy. Later, the prepared NPs were conjugated with cysteamine–folic acid and utilized as a contrast medium for in vitro targeted imaging of folic acid receptor-expressing malignant cells by computerized tomography (CT). At 80 kVp, the targeted cells exhibited CT values which were two times greater than that of the non-targeted cells. The results were compared with the folic acid-negative cell lines as well as the effective inhibition of folic acid receptor using free folic acid substrate. The outcome of the present study suggests that the fabricated cysteamine–folic acid-conjugated silver nanoparticles could be utilized as a potential contrast agent for molecular CT imaging. This information can be taken into consideration for applying AgNPs in enhancing radiation dose where nanoparticles containing greater X-ray attenuation were applied. [ABSTRACT FROM AUTHOR]

Introduction: Computational modeling is one of the best non-invasive approaches to predicting the functional behavior of the mitral valve (MV) in health and disease. Mitral valve prolapse (MVP) due to partial or complete chordae tendineae rapture is the most common valvular disease and results in mitral regurgitation (MR). Methods: In this study, Image-based fluid-structure interaction (FSI) models of the human MV are developed in the normal physiological and posterior leaflet prolapse conditions. Detailed geometry of the healthy human MV is derived from Computed Tomography imaging data. To provide prolapse condition, some chords attached to the posterior leaflet are removed from the healthy valve. Both normal and prolapsed valves are embedded separately in a straight tubular blood volume and simulated under physiological systolic pressure loads. The Arbitrary Lagrangian-Eulerian finite element method is used to accommodate the deforming intersection boundaries of the blood and MV. Results: The stress values in the mitral components, and also flow patterns including the regurgitant flow rates are obtained and compared in both conditions through the simulation. These simulations have the potential to improve the treatment of patients with MVP, and also help surgeons to have more realistic insight into the dynamics of the MV in health and prolapse. Conclusion: In the prolapse model, computational results show incomplete leaflet coaptation, higher MR severity, and also a significant increment of posterior leaflet stress compared to the normal valve. Moreover, it is found more deviation of the regurgitant jet towards the left atrium wall due to the posterior leaflet prolapse.

Fiber-reinforced plastics are lightweight materials expected to reduce greenhouse gases and the fatigue properties need to be clarified to extend the practical applications. However, the fatigue tests have been limited to the surface or cross-sectional observation of cut specimens. In this study, we conducted non-destructive through-thickness measurements using synchrotron radiation X-ray laminography to investigate the internal fatigue damage behavior in sharply notched carbon fiber (30 wt%)-reinforced polyamide 6 specimens. Under fatigue loading, a large number of voids in the matrix and delaminations at the carbon fiber sides as well as fiber ends were nucleated near the notch tip. Incremental fatigue cycles caused voids coalescence, resulting in the extension of the damaged zone. The unit volume of voids increased locally in severely damaged zone due to coalescence of the voids, while it decreased in adjacent locations because the residual voids were small, which resulted in the larger diversity of unit volume of voids. In the specimen thickness direction, the damage zone was larger near the surfaces in plane-stress condition than inside the specimen in plane-strain condition.

A 21-year-old girl presented with an episode of ventricular tachycardia in September 1991. She was found to have a large (8 cm × 5 cm) mass in the right ventricle and was operated. The mass was full of thrombi and fresh blood that was evacuated. She remained asymptomatic till May 2022, when she presented with another episode of ventricular tachycardia. She also had a history of effort angina of 2 months' duration. Multimodality imaging evaluation revealed a large calcific cystic mass in the right ventricular free wall with an unruptured aneurysm of the right sinus of Valsalva, a markedly distorted right ventricular cavity, and the Brugada pattern of electrocardiogram. The case is reported for its rarity, long follow-up, and strikingly high-quality images.

Introduction: Computational modeling is one of the best non-invasive approaches to predicting the functional behavior of the mitral valve (MV) in health and disease. Mitral valve prolapse (MVP) due to partial or complete chordae tendineae rapture is the most common valvular disease and results in mitral regurgitation (MR). Methods: In this study, Image-based fluid-structure interaction (FSI) models of the human MV are developed in the normal physiological and posterior leaflet prolapse conditions. Detailed geometry of the healthy human MV is derived from Computed Tomography imaging data. To provide prolapse condition, some chords attached to the posterior leaflet are removed from the healthy valve. Both normal and prolapsed valves are embedded separately in a straight tubular blood volume and simulated under physiological systolic pressure loads. The Arbitrary Lagrangian-Eulerian finite element method is used to accommodate the deforming intersection boundaries of the blood and MV. Results: The stress values in the mitral components, and also flow patterns including the regurgitant flow rates are obtained and compared in both conditions through the simulation. These simulations have the potential to improve the treatment of patients with MVP, and also help surgeons to have more realistic insight into the dynamics of the MV in health and prolapse. Conclusion: In the prolapse model, computational results show incomplete leaflet coaptation, higher MR severity, and also a significant increment of posterior leaflet stress compared to the normal valve. Moreover, it is found more deviation of the regurgitant jet towards the left atrium wall due to the posterior leaflet prolapse. [ABSTRACT FROM AUTHOR]

Due to the complexity and heterogeneity of cancer, the clear and accurate tumor imaging and image‐guided multimodal synergistic therapy are highly in demand. Here, bismuth (Bi)‐based mesoporous‐silica‐coated nanoparticles (BMSNs) are successfully fabricated with the silica shell thickness being lower than the Bi nanoparticle diameter. Then, an MCF‐7 breast cancer‐targeting peptide (termed AR) is modified onto the surface of BMSNs. The obtained nanoparticles (BMSN‐AR) show a significantly higher loading of doxorubicin hydrochloride (DOX). The resultant BMSN‐AR‐DOX can agglomerate in the tumor site, enabling enhanced computed tomography (CT) imaging of the whole tumor. Under 808 nm near‐infrared (NIR) laser irradiation, the BMSN‐AR‐DOX also effectively converts light energy into thermal energy to achieve synergistic chemo‐photothermal therapy in vivo. The chemo‐photothermal combination therapy is found to be more effective than chemotherapy or photothermal therapy alone. This work demonstrates that tumor‐homing peptides can guide nanoparticles to tumors and allow the nanoparticles to enhance cancer imaging and photothermal/chemo combination therapy. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) is the gold standard for analyzing muscle parameters. Purpose: To clarify sex-specific paraspinal muscle area (PMA), paraspinal muscle index (PMI), and muscle fat infiltration (MFI) percentiles. Material and Methods: This was a cross-sectional study of 760 individuals (45% men; age range = 20–92 years; mean age = 53.4 ± 21.1 years) with a body mass index (BMI) in the range of 16.4–38.1 kg/m2. CT scans were retrospectively used to establish PMA, PMI, and MFI at L3 level using a deep-learning (DL) tool. Sex-specific distributions for these parameters were assessed based on associations between age/BMI and individual muscle parameters, after which age- and BMI-specific percentile estimates were determined. The 5th percentile was regarded as the cutoff for PMA/PMI, and the 95th percentile was regarded as the cutoff for MFI. Results: Sex-specific PMA, PMI, and MFI cutoffs in the paraspinal muscles group were 52.9 cm2, 15.0 cm2/m2, and 33.3%, respectively, in men, and 33.2 cm2, 9.5 cm2/m2, and 41.2% in women. Age was moderately negatively correlated with PMA and was strongly negatively correlated with PMI, but age was strongly positively correlated with MFI. BMI was moderately positively correlated with PMA/PMI in men and strongly positively correlated in women; BMI was weakly positively correlated with MFI, thus enabling the establishment of age- and BMI-specific cutoff percentiles. Conclusion: Sex-specific PMA, PMI, and MFI percentiles and age- and BMI-specific cutoff values for these parameters were successfully established for an outpatient population. [ABSTRACT FROM AUTHOR]

Background: The ideal position of suture annuloplasty relative to the aortic annulus (internal or external) remains unclear. This study aimed to investigate the effectiveness of internal and external suture annuloplasty for isolated type 1 bicuspid aortic valve (BAV) repair. Electrocardiogram (ECG)-gated computed tomography (CT) was used to compare the two techniques and analyze their impact on the aortic annulus. Methods: We retrospectively analyzed 20 patients who underwent isolated type 1 BAV repair with either internal or external suture annuloplasty. Each group included 10 patients with comparable clinical features. Preoperative and postoperative ECGgated CT scans were performed to assess the anatomical relationship between the ventricular-aortic junction (VAJ) and virtual basal ring (VBR), and to measure the height of annuloplasty from the VBR at predefined landmarks in both groups. Perioperative annular geometries, including annular area and perimeter, were measured to quantify the impact of annuloplasty on annular expansibility. The discrepancy between the postoperative annular dimension and size of the Hegar dilator were compared between groups to evaluate the effectiveness of annuloplasty. Results: In both groups, VAJ was higher than VBR at the right coronary (RC) ostium (7.7 ± 3.3 mm) and the raphe (7.9 ± 1.5 mm). The height from the VBR to the external suture annuloplasty shared a similar pattern at the RC ostium and raphe (5.3 ± 1.1 mm and 4.8 ± 1.0 mm, respectively). In contrast, the height differences were minimal for these landmarks in the internal group. Postoperative annular area expansibility decreased in the internal group compared to preoperative levels (4.9 ± 2.3% vs. 8.9 ± 5.5%, p = 0.038), while no significant change was found in the external group (7.6 ± 4.1% vs. 6.5 ± 2.8%, p = 0.473). The internal group showed less area discrepancy between the VBR and the Hegar dilator both at systole (10.1 ± 3.7% vs. 30.1 ± 16.6%, p = 0.004) and diastole (5.7 ± 4.9% vs. 20.9 ± 14.5%, p = 0.009) compared to the external group. Conclusions: Internal suture annuloplasty results in better positioning relative to the VBR than external suture annuloplasty due to the absence of VAJ interference. While this results in more precise annular reduction and less expansibility in the short term, a long-term follow-up evaluation is necessary to assess its effectiveness. [ABSTRACT FROM AUTHOR]

Zhe Liu,1– 3,&ast; Dong Zhou,4,&ast; Xuan Yan,1 Lan Xiao,5,6 Pei Wang,1– 3 Junchao Wei,1– 4 Lan Liao1– 3 1The Affiliated Stomatological Hospital of Nanchang University, Nanchang, People’s Republic of China; 2The Key Laboratory of Oral Biomedicine, Nanchang, People’s Republic of China; 3Jiangxi Province Clinical Research Center for Oral Diseases, Nanchang, People’s Republic of China; 4School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, People’s Republic of China; 5School of Mechanical, Medical & Process Engineering, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, Australia; 6Australia China Centre for Tissue Engineering and Regenerative Medicine, Kelvin Grove, Brisbane, Australia&ast;These authors contributed equally to this workCorrespondence: Junchao Wei; Lan Liao, Email weijunchao@ncu.edu.cn; Liaolan5106@ncu.edu.cnPurpose: The translation of nanocarrier-based theranostics into cancer treatment is limited by their poor cellular uptake, low drug-loading capacity, uncontrolled drug release, and insufficient imaging ability.Methods: In this study, novel hybrid nanogels were fabricated as theranostic nanocarriers by modifying chitosan (CTS)/tripolyphosphate (TPP) nanoparticles (NPs) with polyacrylic acid (PAA) and further conjugating cysteine-functionalized gold nanoparticles (AuNPs).Results: The resultant nanogels, referred to as CTS/TPP/PAA@AuNPs (CTPA), exhibited excellent colloidal stability and a high encapsulation rate of 87% for the cationic drug doxorubicin (DOX). In the tumour microenvironment, the acidic pH and overexpression of lysozyme triggered CTPA@DOX to degrade and emit smaller nanoblocks (30– 40 nm), which sequentially released the drug in a tumour-responsive manner. Cellular uptake experiments demonstrated that CTPA facilitates the entry of DOX into the cytoplasm. Furthermore, as visualised through AuNP-mediated computed tomography (CT) imaging, CTPA@DOX enabled favourable accumulation in the tumour. Our in vitro and in vivo data demonstrated that CTPA enabled advanced tumour cell-targeting delivery of DOX, which showed greater anti-tumour activity and biosafety than free DOX.Conclusion: The natural polymer CTS was developed for degradable nanogels, which can precisely track drugs with high antitumour activity. Additionally, the surface adjustment strategy can be assembled to achieve cationic drug loading and high drug-loading capacity, controlled drug release, and sufficient imaging ability. Therefore, multifunctional CTPA enables efficient drug delivery and CT imaging, which is expected to provide a valuable strategy for designing advanced theranostic systems.Keywords: theranostic nanomaterial, degradability, computed tomography imaging, hybrid nanogel

Artificial intelligence has captivated the healthcare industry as these techniques are efficient and accurate in medical image analysis. In recent years, several deep learning models have been proposed for the task of 2D or 3D bio-medical image segmentation. Liver tumour segmentation in Computed Tomography (CT) images remains an arduous problem due to the tumour's shape variance, its occurrence in multiple regions and the granular noise of the CT images. In this paper, a novel Convolutional Encoder-Decoder Residual Neural Network (CEDRNN) is proposed for automatic liver tumour segmentation from CT images. The feature accumulation using additive skip connections in the proposed model assures better learning of the model and faster convergence during training. A public dataset (3DIRCADb) was used to train and test the proposed model. The 3DIRCADb dataset is considered challenging to use because it contains a greater variety and complexity of livers and tumours. The CT slices were subjected to pre-processing steps such as Hounsfield windowing and Gaussian filtering. The proposed model has resulted in an average DICE score of 95.2% for liver tumour segmentation. The task of tumour segmentation is done precisely without the need for prior liver segmentation in the CT image. Despite having 85 layers in its architecture, CEDRNN outperforms comparable models with only 0.6 million network parameters. The experimental results proved that the proposed model is more efficient for liver tumour segmentation as compared to the related existing approaches. The automatic segmentation of the tumour is essential for the clinical interpretation and diagnosis of liver-related problems. [ABSTRACT FROM AUTHOR]

Current decision tools to guide trauma computed tomography (CT) imaging were not validated for use in older patients. We hypothesized that specific clinical variables would be predictive of injury and could be used to guide imaging in this population to minimize risk of missed injury. Blunt trauma patients aged 65 y and more admitted to a Level 1 trauma center intensive care unit from January 2018 to November 2020 were reviewed for histories, physical examination findings, and demographic information known at the time of presentation. Injuries were defined using the patient's final abbreviated injury score codes, obtained from the trauma registry. Abbreviated injury score codes were categorized by corresponding CT body region: Head, Face, Chest, C-Spine, Abdomen/Pelvis, or T/L-Spine. Variable groupings strongly predictive of injury were tested to identify models with high sensitivity and a negative predictive value. We included 608 patients. Median age was 77 y (interquartile range, 70-84.5) and 55% were male. Ground-level fall was the most common injury mechanism. The most commonly injured CT body regions were Head (52%) and Chest (42%). Variable groupings predictive of injury were identified in all body regions. We identified models with 97.8% sensitivity for Head and 98.8% for Face injuries. Sensitivities more than 90% were reached for all except C-Spine and Abdomen/Pelvis. Decision aids to guide imaging for older trauma patients are needed to improve consistency and quality of care. We have identified groupings of clinical variables that are predictive of injury to guide CT imaging after geriatric blunt trauma. Further study is needed to refine and validate these models. [ABSTRACT FROM AUTHOR]

Background: Muscle quantification is an essential step in sarcopenia evaluation. Purpose: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on either abdominal or lumbar (L) computed tomography (CT) scans. Material and Methods: A novel deep neural network algorithm for automated segmentation of paraspinous muscle was developed, CT scans of 504 consecutive patients conducted between January 2019 and February 2020 were assembled. The muscle was manually segmented at L3 vertebra level by three radiologists as ground truth, divided into training and testing subgroups. Muscle cross-sectional area (CSA) was recorded. Dice similarity coefficients (DSCs) and CSA errors were calculated to evaluate system performance. The degree of muscle fat infiltration (MFI) recording by percentage value was the fat area within the region of interest divided by the muscle area. An analysis of the factors influencing the performance of the V-net-based segmentation system was also implemented. Results: The mean DSCs for paraspinous muscles were high for both the training (0.963, 0.970, 0.941, and 0.968, respectively) and testing (0.950, 0.960, 0.929, and 0.961, respectively) datasets, while the CSA errors were low for both training (1.9%, 1.6%, 3.1%, and 1.3%, respectively) and testing (3.4%, 3.0%, 4.6%, and 1.9%, respectively) datasets. MFI and muscle area index (MI) were major factors affecting DSCs of the posterior paraspinous and paraspinous muscle groups. Conclusion: The ML algorithm for the measurement of paraspinous muscles was compared favorably to manual ground truth measurements. [ABSTRACT FROM AUTHOR]

Acute Mesenteric Ischemia (AMI) is a rare life-threatening entity caused by sudden interruption of the blood supply to a segment of the bowel due to impairment of mesenteric arterial blood flow or venous drainage. Clinical presentation varies according to the time course of vascular occlusion. Contrast-enhanced Computed Tomography (CT) of the abdomen represents the main diagnostic test for AMI diagnosis, enabling fast and excellent evaluation of the intestine, mesenteric vasculature, and other ancillary characteristics of AMI. Typical CT findings of AMI include paralytic ileus, decreased or absent bowel wall contrast-enhancement, pneumatosis intestinalis, and porto-mesenteric venous gas. We hereby report a case of an 89-year-old man presenting with AMI due to Superior Mesenteric Artery (SMA) thrombotic occlusion following endovascular stenting superficial femoral arteries. Typical findings were observed on abdominal CT imaging, yet associated with the presence of gas exclusively in the SMA district, without any involvement of the porto-mesenteric venous system. Different imaging features and pitfalls can help radiologists to accurately diagnose AMI, especially when irreversible bowel damage is about to occur. Therefore, radiologists and emergency physicians should be aware of the unusual association between gas in the SMA arterial district and AMI, even in the absence of porto-mesenteric venous system involvement, in order to urge prompt surgical consultation when observed.

This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes. In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.7 % men) underwent chest CT to assess PE. We investigated PE's association with 30-day mortality, ICU admission, and the need for mechanical ventilation. PE was detected in 31.5 % of patients, showing a significant correlation with 30-day mortality (47.5 % in non-survivors vs. 27.3 % in survivors, p < 0.001), with a hazard ratio of 2.22 (95 % CI 1.65–2.99, p < 0.001). No significant association was found between PE volume or density and mortality. ICU admissions were noted in 46.8 % of patients, while mechanical ventilation was required for 26.7 %. Pleural effusion is present in a significant portion of COVID-19 patients and independently predicts increased 30-day mortality, underscoring its value as a prognostic marker. Its identification, irrespective of volume or density, should be a priority in radiological reports to guide clinical decision-making. • Large multicentric study of overall 1183 patients • A relevant prognostic information is provided by the occurrence of pleural effusion. • Volumetric and densitometric analyses [ABSTRACT FROM AUTHOR]

The present work proposes entropy based automatic unsupervised brain intracranial hemorrhage segmentation using CT images. The proposed work is consisting of fuzzy c-mean (FCM), automatic selection of cluster, skull removal, thresholding and edge-based active contour methods. The FCM is used to divide the image into different cluster and among these clusters one is automatically selected for the segmentation process which have skull and hemorrhage region. Due to major benefits of level set method i.e. use large time step which reduce the number of iterations and does not need reinitialization process thus it has fast convergence speed is used for smoothness of hemorrhage region. The seed point for level set method is initialized by entropy based thresholding techniques.The exhaustive experimentations have been carried out on 35 different patients CT images collected from SGRR Institute of Medical & Health Sciences and SMI Hospital, Dehradun, Uttarakhand, India. The performance of developed automatic segmentation techniques for hemorrhage detection is analyzed by using sensitivity, specificity, Dice coefficient, Jaccard index, Precision and Accuracy. After the evaluation of proposed method it can be observed that hemorrhagic regions segmented by the proposed method, has high accuracy comparison to FCM method and manual fuzzy based active contour method.

Bismuth (Bi)-based computed tomography (CT) imaging contrast agents (CAs) hold significant promise for diagnosing gastrointestinal diseases due to their cost-effectiveness, heightened sensitivity, and commendable biocompatibility. Nevertheless, substantial challenges persist in achieving an easy synthesis process, remarkable water solubility, and effective targeting ability for the potential clinical transformation of Bi-based CAs. Herein, we show Bi drug-inspired ultra-small dextran coated bismuth oxide nanoparticles (Bi 2 O 3 -Dex NPs) for targeted CT imaging of inflammatory bowel disease (IBD). Bi 2 O 3 -Dex NPs are synthesized through a simple alkaline precipitation reaction using bismuth salts and dextran as the template. The Bi 2 O 3 -Dex NPs exhibit ultra-small size (3.4 nm), exceptional water solubility (over 200 mg mL−1), high Bi content (19.75 %), excellent biocompatibility and demonstrate higher X-ray attenuation capacity compared to clinical iohexol. Bi 2 O 3 -Dex NPs not only enable clear visualization of the GI tract outline and intestinal loop structures in CT imaging but also specifically target and accumulate at the inflammatory site in colitis mice after oral administration, facilitating a precise diagnosis and enabling targeted CT imaging of IBD. Our study introduces a novel and clinically promising strategy for synthesizing high-performance Bi 2 O 3 -Dex NPs for diagnosing gastrointestinal diseases. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Background Advances in chest computed tomography (CT) have resulted in more frequent detection of subcentimeter pulmonary nodules (SCPNs), some of which are non‐benign and may represent invasive lung cancer. The present study aimed to explore the correlation between pathological diagnosis and the CT imaging manifestations of SCPNs. Methods This retrospective study included patients who underwent pulmonary resection for SCPNs at Shandong Provincial Hospital in China. Lesions were divided into five categories according to their morphological characteristics on CT: cotton ball, solid‐filled with spiculation, solid‐filled with smooth edges, mixed‐density ground‐glass, and vacuolar. We further analyzed lesion size, enhancement patterns, vascular aggregation, and SCPN traversing. Chi‐square tests, Fisher's exact tests, and Welch's one‐way analysis of variance were used to examine the correlation between CT imaging characteristics and pathological type. Results There were statistically significant differences in the morphological distributions of SCPNs with different pathological types, including benign lesions and malignant lesions at different stages (p

Vascular air embolism (VAE) is an uncommon but potentially life-threatening event often associated with various procedures and diagnostic tests. Understanding this challenging incident will enable physicians to evaluate, diagnose and manage this incident successfully. We present a case of a 73-year-old male who developed air in the right ventricle after intravenous contrast-enhanced computed tomography (CT) imaging. We aim at highlighting this challenging clinical scenario and emphasizing ways to assess the risks and implement means to prevent and treat accordingly.

• What is known about this topic? CT imaging in children can cause radiation-induced malignancies later in life. Guidelines can assist providers in determining imaging indicated for injured children. • What new information is contained in this article? Quality improvement and implementation science methodologies enhance guideline implementation efforts. Prospectively tracking adherence and iterative process improvement efforts can sustain and further improve guideline compliance over time. "Pan-scanning" pediatric blunt trauma patients leads to exposure to harmful radiation and increased healthcare costs without improving outcomes. We aimed to reduce computed tomography (CT) scans that are not indicated (NI) by imaging guidelines for injured children. In July 2017, our Pediatric Trauma Center prospectively implemented validated imaging guidelines to direct CT imaging for trauma activations and consultations for children younger than 16 years old with blunt traumatic injuries. Patients with suspected physical abuse, CT imaging prior to arrival, penetrating mechanism, and instability precluding CT imaging were excluded. We compared CT scanning rates for pre-implementation (01/2016–06/2017) and post-implementation (07/2017–08/2021) time periods. Guideline compliance was evaluated by chart review and sustained through iterative process improvement cycles. During the pre-implementation era, 61 patients underwent 171 CT scans of which 87 (51%) scans were not indicated by guidelines. Post-implementation, 363 patients had 531 scans and only 134 (25%) CTs were not indicated. Total CTs performed declined after initiation of guidelines (2.80 vs 1.46 scans/patient, p <0.0001). Total NI CTs declined (1.41 vs 0.37 NI scans/patient, p <0.0001) reflected in significant reductions in all anatomic regions: head, cervical spine, chest, and abdomen/pelvis. Charges related to NI scans decreased from $1,490.31/patient to $408.21/patient, saving $218,000 in charges. Based on prior utilization, 146 children were spared excessive radiation with no clinically significant missed injuries since guideline implementation. Quality improvement and implementation science methodologies to enhance compliance with imaging guidelines for children with blunt injuries can significantly reduce unnecessary CT scanning without compromising care. This practice reduces harmful radiation exposure in a sensitive patient population and may save healthcare systems money and resources. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to determine possible associations between sarcopenia and poor cardiovascular outcomes in patients with chronic heart failure after cardiac resynchronization therapy. Methods: This retrospective study evaluated 120 patients who underwent cardiac resynchronization therapy between March 2004 and June 2018. In total, 58 patients who underwent computed tomography within 30 days of cardiac resynchronization therapy implantation were eligible for inclusion, and their data were analyzed (25 women; 33 men; mean age 71.6 ± 8.7 years). Skeletal muscle area was measured at the third lumbar vertebra, and skeletal muscle index was calculated. Major adverse cardiovascular events included cardiovascular death, hospitalization due to heart failure, cerebral infarction, acute myocardial infarction and cardiac arrest. Results: During the follow‐up period (mean 868 ± 617 days), major adverse cardiovascular events occurred in 22 of 58 patients (38%). The patients were allocated to two groups according to sex‐based tertiles of skeletal muscle index. The lowest tertile was defined as the low skeletal muscle index group. Kaplan–Meier survival analysis showed that the low skeletal muscle index group had a higher incidence of major adverse cardiovascular events (log‐rank 4.38; P = 0.036). Cox proportional hazards regression analysis also showed that low skeletal muscle index values were significantly associated with major adverse cardiovascular events (hazard ratio 3.08; 95% confidence interval 1.26–7.66, P = 0.014). Conclusions: Decreases in skeletal mass index on computed tomography might predict the occurrence of major adverse cardiovascular events in patients with chronic heart failure who underwent cardiac resynchronization therapy. Geriatr Gerontol Int 2022; 22: 1013–1018. [ABSTRACT FROM AUTHOR]

Purpose: Lumbosacral transitional vertebra (LSTV) is a congenital anomaly of the lumbosacral junction. Its prevalence is variable in the literature such as its association with low back pain. The aim of this study was to identify the prevalence of LSTV in a southern European population, and its correlation with low back pain. Methods: A retrospective review of 639 thoraco-abdomino-pelvic consecutive CT-scans between January 2019 and November 2020 was performed. The presence of LSTV was classified into type II, III, IV based on Castellvi's classification. To investigate the association with low back pain, Oswestry Low Back Disability Questionnaire (ODI) and the EuroQol-5D-3L questionnaire was applied. Results: The prevalence of LSTV was 24.9% (142 of 571). 37,3% were type IIb, 31,0% were type IIa, 13,4% were type IIIa, 9.9% were type IIIb and 8.5% were type IV. Individuals with LSTV were more likely to report low back pain and have a higher ODI score (OR:0.392, 95% CI:0.192–0.802, p = 0.010), (OR: 1050, 95% CI: 1029–1072, p < 0.01). Castellvi's type IV showed a significantly higher ODI when compared to type II (OR:1059, 95% CI:1019–1100, p = 0,04). There was no statistical difference in the EuroQol-5D-3L score between two groups (OR:1085, 95% CI: 0.459–2.560, p = 0.852). Conclusion: This population-based study adds to the literature the prevalence of LSTV in a southern European population. LSTV was associated with low back pain. However, this difference did not translate into a loss of quality life. Type IV was associated with higher functional disability when compared with type II. [ABSTRACT FROM AUTHOR]

Laryngomalacia is the top cause of pediatric laryngeal wheeze. We used computational fluid dynamics to study the inspiratory airflow dynamics in severe pediatric laryngomalacia. Computed tomography was performed on the upper airways of two infants, one with severe laryngomalacia and one with normal airway, and 3D models were reconstructed. ANSYS CFD-POST software was used to simulate airflow in these models to compare the volumetric flow rate, flow velocity, pressure, wall shear, and vortex. The volume flow rate in the laryngomalacia model was significantly reduced compared with the control model. Under inspiratory pressures, the peak flow velocity, pressure, and shear force in the control model appeared at the soft palate stenosis, while that in the laryngomalacia model appeared at the supraglottis stenosis. In both models, the maximum flow velocity and shear force increased with decreasing inspiratory pressure, while the minimum pressure decreased with decreasing inspiratory pressure. In the control model, the airflow vortex appeared anteriorly below the posterior section of the soft palate. In the laryngomalacia model, the vortex appeared anteriorly below the posterior section of the soft palate and anteriorly below the vocal folds. Our methodology provides a new mechanistic understanding of pediatric laryngomalacia. [ABSTRACT FROM AUTHOR]

OBJECTIVE: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. METHODS: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework as a regularization term to construct the objective function. The group-based sparse representation no longer takes a single patch as the minimum unit of sparse representation, while it uses Euclidean distance as a similarity measure, thus it divides similar patch into groups as basic units for sparse representation. This method fully considers the local sparsity and non-local self-similarity of image. The proposed method is compared with several commonly used CT image reconstruction methods including FBP, SART, SART-TV and GSR-SART with experiments carried out on Sheep_Logan phantom and abdominal and pelvic images. RESULTS: In three experiments, the visual effect of the proposed method is the best. Under 64 projection angles, the lowest RMSE is 0.004776 and the highest VIF is 0.948724. FSIM and SSIM are all higher than 0.98. Under 50 projection angles, the index of the proposed method remains achieving the best image quality. CONCLUSION: Qualitative and quantitative results of this study demonstrate that this new proposed method can not only remove strip artifacts, but also effectively protect image details. [ABSTRACT FROM AUTHOR]

Introduction: Body composition evaluation can be used to assess patients' nutritional status to predict clinical outcomes. To facilitate reliable and time-efficient body composition measurements eligible for clinical practice, fully automated computed tomography segmentation methods were developed. The aim of this study was to evaluate automated segmentation by Data Analysis Facilitation Suite in an independent dataset., Materials and Methods: Preoperative computed tomography images were used of 165 patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy from 2014 to 2019. Manual and automated measurements of skeletal muscle mass (SMM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) were performed at the third lumbar vertebra. Segmentation accuracy of automated measurements was assessed using the Jaccard index and intra-class correlation coefficients., Results: Automatic segmentation provided accurate measurements compared to manual analysis, resulting in Jaccard score coefficients of 94.9 for SMM, 98.4 for VAT, 99.1 for SAT, and 79.4 for IMAT. Intra-class correlation coefficients ranged from 0.98 to 1.00. Automated measurements on average overestimated SMM and SAT areas compared to manual analysis, with mean differences (±2 standard deviations) of 1.10 (-1.91 to 4.11) and 1.61 (-2.26 to 5.48) respectively. For VAT and IMAT, automated measurements on average underestimated the areas with mean differences of -1.24 (-3.35 to 0.87) and -0.93 (-5.20 to 3.35), respectively., Conclusions: Commercially available Data Analysis Facilitation Suite provides similar results compared to manual measurements of body composition at the level of third lumbar vertebra. This software provides accurate and time-efficient body composition measurements, which is necessary for implementation in clinical practice., Competing Interests: Declaration of competing interest Mirza Faisal Beg is a co-founder and actively directs Voronoi Health Analytics Incorporated, a Canadian corporation that sells commercial licenses for the DAFS software. Karteek Popuri is a co-founder and actively directs Voronoi Health Analytics Incorporated, a Canadian corporation that sells commercial licenses for the DAFS software. Vincent Tze Yang Chow is a founding member and oversees operations in Voronoi Health Analytics Incorporated. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Abstract Background The coexistence of sarcopenia and dementia in aging populations is not uncommon, and they may share common risk factors and pathophysiological pathways. This study aimed to evaluate the relationship between brain atrophy and low lean mass in the elderly with impaired cognitive function. Methods This cross-sectional study included 168 elderly patients who visited the multi-disciplinary dementia outpatient clinic at Kaohsiung Chang Gung Memorial Hospital for memory issues, between 2017 and 2019. The body composition was assessed by dual energy X-ray absorptiometry (DEXA) and CT based skeletal muscle index including L3 skeletal muscle index (L3SMI) and masseter muscle mass index (MSMI). The brain atrophy assessment was measured by CT based visual rating scale. Possible predictors of low lean mass in the elderly with cognitive impairement were identified by binary logistic regression. ROC curves were generated from binary logistic regression. Results Among the 81 participants, 43 (53%) remained at a normal appendicular skeletal muscle index (ASMI), whereas 38 (47%) showed low ASMI. Compared with the normal ASMI group, subjects with low ASMI exhibited significantly lower BMI, L3SMI, and MSMI (all p

Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

Yulan Yan,1,&ast; Chunmei Yang,1,&ast; Guidong Dai,1 Yu Zhang,1 Guojian Tu,1 Yuwei Li,2 Lu Yang,1 Jian Shu1 1Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan Province, People’s Republic of China; 2Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Lu Yang; Jian ShuDepartment of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan Province, People’s Republic of ChinaTel +86 830-2504762; +86 18980253083Email yangluchem@qq.com; shujiannc@163.comBackground: Development of new long-circulating contrast agents for computed tomography (CT) and magnetic resonance imaging (MRI) of different biological systems still remains a great challenge. Here, we report the synthesis of folic acid (FA)-targeted CuFeSe2 nano-contrast agent for CT and MRI imaging in vitro and in vivo.Methods and Results: In our study, CuFeSe2 was fabricated through a facile and green aqueous reaction and then further aminated through silanization. The amine-functionalized CuFeSe2-NH2 nanoparticles enable the covalent conjugation of folate-conjugated polyethylene glycol (FA-PEG-COOH) as a targeting ligand onto their surface, which could improve the dispersion and endue the targetability of nanoparticles, respectively. The formed multifunctional CuFeSe2-PEG-FA nanoparticles were characterized via different techniques, which exhibited outstanding dispersion, good biocompatibility and excellent FA-targeted capability. Meanwhile, the nanoparticles were quite safe in the given concentration range as confirmed by in vitro and in vivo toxicity assay. Importantly, CuFeSe2-PEG-FA nanoparticles were successfully applied in CT/MRI dual-modality imaging in vitro and in vivo, which showed a better imaging performance and targeted capability.Conclusion: Therefore, the constructed CuFeSe2-PEG-FA nanoparticles have a great potential as an efficient contrast agent for dual-modality imaging of different biological systems.Keywords: CuFeSe2 nanoparticles, magnetic resonance imaging, computed tomography imaging, dual-modality imaging, folic acid

Accurate segmentation of the prostate and surrounding organs at risk (OARs) from CT scans is critical for radiotherapy treatment planning in prostate cancer. However, manual segmentation is time-consuming and prone to variability. This paper proposes a deep learning-based approach using a pre-trained ResNet-18 combined with an encoder–decoder structure based on DeepLabv3+. The method automates the segmentation of the prostate, bladder, and rectum in male pelvic CT scans, achieving precise and efficient results without requiring preprocessing or extensive manual refinement. Evaluated on 100 CT scans using 10-fold cross-validation, the model demonstrates strong performance (Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD)) on prostate (DSC: 84. 32 ± 4. 88 % , HD: 3. 95 ± 0. 60 mm), bladder (DSC: 86. 53 ± 3. 66 % , HD: 4. 58 ± 0. 72 mm), and rectum (DSC: 83. 92 ± 4. 18 % , HD: 2. 99 ± 0. 40 mm) segmentation. Additionally, a user-friendly MATLAB application is developed to automate the segmentation process. This approach has the potential to improve treatment planning efficiency, accuracy, and consistency for better patient outcomes. • Automatically segment prostate, bladder, and rectum in CT images. • Efficient single-step segmentation with accurate results. • High Dice coefficients and low Hausdorff distances achieved. • Precise organ segmentation enhances prostate targeting. • MATLAB app developed for easy automated segmentation. [ABSTRACT FROM AUTHOR]

Study Design This retrospective case series enrolled 13 patients who underwent posterior fixation with both transdiscal screws for diffuse idiopathic skeletal hyperostosis (TSDs) and pedicle screws (PSs) to treat spinal injury accompanied by diffuse idiopathic skeletal hyperostosis (DISH). Purpose To describe the usefulness, feasibility, and biomechanics of TSD. Overview of Literature Vertebral bodies accompanied by DISH generally have lower bone mineral density than normal vertebral bodies because of the stress shielding effect. This phenomenon tends to makes screw fixation challenging. To our knowledge, solutions for this issue have not previously been reported. Methods Patients were assessed using the data on surgical time, estimated intraoperative blood loss, mean number of stabilized intervertebral segments, number of screws used, perioperative complications, union rate, and the three-level EuroQol five-dimensional questionnaire (EQ5D-3L) score at the final follow-up. The Hounsfield unit (HU) values of the screw trajectory area, and the actual intraoperative screw insertion torque of TSDs and PSs were also analyzed and compared. Results The surgical time and estimated intraoperative blood loss were 165.9±45.5 minutes and 71.0±53.4 mL, respectively. The mean number of stabilized intervertebral segments was 4.6±1.0. The number of screws used was 4.9±1.3 for TSDs and 3.0±1.4 for PSs. One death occurred after surgery. The union rate and EQ5D-3L scores were 100% and 0.608±0.128, respectively. The HU value and actual intraoperative screw insertion torque of TSDs were significantly better than those of PSs (p