High-resolution Computed Tomography Images Research Articles

Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

Radiological usual interstitial pneumonia pattern is associated with two-year mortality in patients with idiopathic pulmonary fibrosis

IntroductionThe new diagnostic guidelines for idiopathic pulmonary fibrosis (IPF) did not rule out the possibility of combining the radiological patterns of usual interstitial pneumonia (UIP) and probable UIP, given the similar management and diagnostic capacity. However, the prognostic implications of these patterns have not been fully elucidated, with different studies showing heterogeneous results. We applied the new criteria to a retrospective series of patients with IPF, assessing survival based on radiological patterns, findings, and their extension. MethodsTwo thoracic radiologists reviewed high-resolution computed tomography images taken at diagnosis in 146 patients with IPF, describing the radiological findings and patterns. The association of each radiological finding and radiological patterns with two-year mortality was analysed. ResultsThe two-year mortality rate was 40.2% in IPF patients with an UIP radiological pattern versus 7.1% in those with probable UIP. Compared to the UIP pattern, probable UIP was protective against mortality, even after adjusting for age, sex, pulmonary function, and extent of fibrosis (hazard ratio (HR) 0.23, 95% confidence interval (CI) 0.06–0.99). Receiving antifibrotic treatment was also a protective factor (HR 0.51, 95%CI 0.27–0.98). Honeycombing (HR 3.62, 95%CI 1.27–10.32), an acute exacerbation pattern (HR 4.07, 95%CI 1.84–8.96), and the overall extent of fibrosis (HR 1.04, 95%CI 1.02–1.06) were predictors of mortality. ConclusionsIn our series, two-year mortality was higher in patients with IPF who presented a radiological pattern of UIP versus probable UIP on the initial scan. Honeycombing, an acute exacerbation pattern, and a greater overall extent of fibrosis were also predictors of increased mortality. The prognostic differences between the radiological pattern of UIP and probable UIP in our series would support maintaining them as two differentiated patterns.

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.

Objective: This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). Methods: In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). Results: The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR (P < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR (P < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing (P = .014). Conclusions: DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR.

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation.

Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.

The association between high jugular bulb and mastoid pneumatization in adults.

The purpose of this study was to analyze the relationship between the degree of high jugular bulb (HJB) and mastoid pneumatization using high-resolution computed tomography (HRCT). Between April 2019 and June 2022, HRCT of the temporal bone was retrospectively analyzed in 1,025 patients. By excluding the other coexistent pathologies, 113 patients with HJBs were recruited for the study. The degree of the HJBs were defined as follows: Grade I, JB situated between inferior annulus of tympanic membrane and cochlear basal turn (CBT). Grade II, JB situated between CBT and lateral semicircular canal (LSC). Grade III, JB situated above LSC. The volume of mastoid pneumatization was based on HRCT images using a 3D reconstruction. There were 32 male and 81 female subjects (mean age, 41.2 ± 14.0 years; age range, 18-80 years). The male group included 16 Grade I, 28 Grade II and 6 Group III HJB subjects. The female group included 38 Grade I, 62 Grade II and 31 Group III HJB cases. In the different groups of HJB, the mastoid cell volume differences were also not statistically significant (p = 0.165). In the classification, Grade II was most common (90/181, 49.7%). This study found no correlation between mastoid air cell volume and HJB, suggesting that HJB may not affect the mastoid air cell development and disease occurrence. These data must be considered exploratory, requiring more extensive cross-sectional studies.

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mortality. The interactive paradigm assisted by neural networks is considered to be an effective means for early lung cancer screening in large populations. However, some inherent characteristics of pulmonary nodules in high-resolution CT images, e.g., diverse shapes and sparse distribution over the lung fields, have been inducing inaccurate results. On the other hand, most existing methods with neural networks are dissatisfactory from a lack of transparency. In order to overcome these obstacles, a united framework is proposed, including the classification and feature visualization stages, to learn distinctive features and provide visual results. Specifically, a bilateral scheme is employed to synchronously extract and aggregate global-local features in the classification stage, where the global branch is constructed to perceive deep-level features and the local branch is built to focus on the refined details. Furthermore, an encoder is built to generate some features, and a decoder is constructed to simulate decision behavior, followed by the information bottleneck viewpoint to optimize the objective. Extensive experiments are performed to evaluate our framework on two publicly available datasets, namely, 1) the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and 2) the Lung and Colon Histopathological Image Dataset (LC25000). For instance, our framework achieves 92.98% accuracy and presents additional visualizations on the LIDC. The experiment results show that our framework can obtain outstanding performance and is effective to facilitate explainability. It also demonstrates that this united framework is a serviceable tool and further has the scalability to be introduced into clinical research.

Development and Validation of a Nomogram for Axillary Lymph Node Metastasis Risk in Breast Cancer.

Purpose: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer treatment planning. This study prospectively analyzed risk factors for ALN metastasis by comparing high-resolution computed tomography (HRCT) imaging with pathology and developed a nomogram to aid in diagnosis. Methods: From April 2023 to May 2024, breast cancer patients confirmed by pathology participated in the study. All had chest HRCT before surgery, and ALN samples were anatomically matched to HRCT imaging and pathology. The least absolute shrinkage and selection operator (LASSO) regression helped refine metastasis features, and a nomogram was constructed using the final selected features determined by multivariate logistic regression. The nomogram's performance was evaluated with concordance index (C-index), calibration plot, and decision curve analysis, with internal validation through bootstrapping. Results: A total of 302 ALN from 98 patients were included in this study. The predictors included in the nomogram encompassed the mean CT value, short diameter, border, and shape of ALN, as well as the Ki-67 status and histological grade of the primary tumor. The model exhibited satisfactory discrimination, with a C-index of 0.869 (95% CI: 0.826-0.912) and an AUC of 0.862 (95% CI, 0.815-0.909). The calibration curve demonstrated a high degree of concordance between the predicted and actual probabilities. The decision curve analysis demonstrated that the nomogram was clinically useful when the threshold for intervention was set at the metastasis possibility range of 1% to 86%. Conclusion: The nomogram combined with preoperative pathology and HRCT imaging have the potential to improve the evaluation of ALN status.

Role of Diffusion-weighted Magnetic Resonance Imaging in Differentiating Cholesteatoma from Chronic Suppurative Otitis Media: A Cross-sectional Study

Introduction: Radiological imaging, such as High-resolution Computed Tomography (HRCT) of the temporal bone, has always held significant importance in diagnosing and staging Chronic Suppurative Otitis Media (CSOM). In preoperative cases of CSOM and cholesteatoma, HRCT describes the disease’s extent to aid in surgical approach, while diffusion-weighted Magnetic Resonance Imaging (MRI) sequences can differentiate cholesteatoma from other common inflammatory middle ear diseases. Aim: To determine the role of Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) in differentiating between granulation tissue and cholesteatoma using histopathological findings as a reference standard. Materials and Methods: A hospital-based cross-sectional study was conducted in the Department of Radiodiagnosis, Sri Manakula Vinayagar Medical College, Puducherry, India, from November 2018 to April 2020. A total of 40 patients who underwent HRCT of the temporal bone for preoperative evaluation of CSOM underwent diffusion sequence MRI, and their findings were compared with intraoperative and postoperative histopathological results. Findings such as soft-tissue masses with scutum and erosion of ear ossicles were analysed in HRCT, while diffusion restriction was observed in DW-MRI in cases of cholesteatoma. Data were collected using a prescribed proforma and analysed using Statistical Package for Social Sciences (SPSS) software version 24.0. Results: The mean±Standard Deviation (SD) age of the patients was 43±20.41 years (27 males, 13 females). The most common presentations in this study were mucopurulent discharge and conductive hearing loss (20 out of 40 patients). Among the 16 patients diagnosed with cholesteatoma through histopathological examination, all (100%) were reported to have diffusion restriction present in the MRI. The sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of DWI were all found to be 100% in diagnosing cholesteatoma. The sensitivity of HRCT was 81.25% and the specificity was 83.3%. Conclusion: High-resolution computed tomography provides a surgical roadmap indicating the extent of involvement and bony structures, while DW-MRI imaging helps in differentiating cholesteatoma from CSOM. A combined preoperative approach using HRCT and DW-MRI imaging can aid in diagnosing cholesteatoma effectively.

Examination of ethmoidal roof regarding Keros and Yenigun classifications in a Turkish population: a computerized tomography study.

Understanding ethmoid roof morphology is crucial to prevent complications in endoscopic sinus surgery. This study aimed to evaluate the morphological properties of the ethmoidal roof regarding gender and age differences using Keros and Yenigun classifications on high-resolution computed tomography images. We retrospectively analyzed 891 high-resolution computed tomography paranasal sinus study images and measured the depth of the cribriform plate in coronal sections and the anterior-posterior length in axial planes. The study retrospectively examined CT images of paranasal sinuses of patients living in the eastern Anatolian region of Turkey. In both Keros and Yenigun Classifications, the most common class was type 2, and the least common class was type 3. According to Keros et al.'s method, no significant difference was observed between men and women (p = 0.698). However, according to Yenigun et al., the average values of women in terms of the anterior-posterior distance of the ethmoid roof were significantly higher than men (p = 0.001). When examined according to age, a very low, negative correlation was revealed regarding Keros and Yenigun classifications (p = 0.047 and p < 0.001 retrospectively). According to Keros and Yenigun's classification, there was no significant difference between the left and right sides (p = 0.488 and p = 0.919, respectively). The morphological properties of the ethmoidal roof have importance to be considered for preoperative planning. Studying larger patient groups and meta-analyses that gather various research results about this subject might help better understand the ethmoidal roof morphology among populations.

Image factory: A method for synthesizing novel CT images with anatomical guidance.

Deep learning in medical applications is limited due to the low availability of large labeled, annotated, or segmented training datasets. With the insufficient data available for model training comes the inability of these networks to learn the fine nuances of the space of possible images in a given medical domain, leading to the possible suppression of important diagnostic features hence making these deep learning systems suboptimal in their performance and vulnerable to adversarialattacks. We formulate a framework to address this lack of labeled data problem. We test this formulation in computed tomographic images domain and present an approach that can synthesize large sets of novel CT images at high resolution across the full Hounsfield (HU)range. Our method only requires a small annotated dataset of lung CT from 30 patients (available online at the TCIA) and a large nonannotated dataset with high resolution CT images from 14k patients (received from NIH, not publicly available). It then converts the small annotated dataset into a large annotated dataset, using a sequence of steps including texture learning via StyleGAN, label learning via U-Net and semi-supervised learning via CycleGAN/Pixel-to-Pixel (P2P) architectures. The large annotated dataset so generated can then be used for the training of deep learning networks for medical applications. It can also be put to use for the synthesis of CT images with varied anatomies that were nonexistent within either of the input datasets, enriching the dataset evenfurther. We demonstrate our framework via lung CT-Scan synthesis along with their novel generated annotations and compared it with other state of the art generative models that only produce images without annotations. We evaluate our framework effectiveness via a visual turing test with help of a few doctors and radiologists. We gain the capability of generating an unlimited amount of annotated CT images. Our approach works for all HU windows with minimal depreciation in anatomical plausibility and hence could be used as a general purpose framework for annotated data augmentation for deep learning applications in medical imaging.

Residual lung abnormalities in recovered COVID-19 cases at 1-year follow-up: A single-center final radiological outcome phenotype study in India

Abstract INTRODUCTION: The radiological phenotype represents the radiological characteristics of coronavirus disease 2019 (COVID-19) pneumonia. Radiological outcomes initially interpreted as post-COVID-19 lung fibrosis have been detected in short-term follow-up of recovered COVID-19 cases, but long-term radiological evaluation suggests that these abnormalities should be defined as post-COVID-19 sequelae. The present study aimed to evaluate the radiological phenotypes of post-COVID-19 residual radiological lung abnormalities (RLAs) in recovered COVID-19 cases. METHODS: This prospective, observational study included 3000 patients with polymerase chain reaction-confirmed COVID-19, with documented lung involvement and radiological severity categorized as mild, moderate, or severe by high-resolution computed tomography (HRCT) at admission. Final radiological outcomes were based on radiological assessments at 6 months and 1 year after hospital discharge. Final radiological phenotypes were defined as RLAs and categorized as resolving, persistent, or progressive, based on HRCT imaging at 1 year of follow-up. A total of 622 cases with RLAs were finally included. The final outcome phenotypes were evaluated in relation to age, sex, comorbidities, laboratory parameters, and interventions, including oxygen and ventilator-support requirements during hospitalization. The data were analyzed using Chi-square tests. RESULTS: RLAs were documented in 622 of the 3000 COVID-19 pneumonia cases (20.73%). The final radiological outcomes were defined as resolving in 77.17% (480/622), persistent in 15.43% (96/622), and progressive in 7.39% (46/622). Radiological outcome phenotypes and RLAs were significantly associated with the duration of illness at admission (P &lt; 0.00001), laboratory parameters (D-dimer, C-reactive protein, and interleukin-6) at admission (P &lt; 0.00001), radiological severity on computed tomography (P &lt; 0.00001), and inpatient interventions (P &lt; 0.00001). Radiological outcome phenotypes and RLAs were also significantly associated with age, sex, diabetes mellitus, ischemic heart disease, hypertension, chronic obstructive pulmonary disease, obesity, and lung functions assessed by spirometry at 1-year follow-up (P &lt; 0.00001). CONCLUSIONS: Radiological outcome phenotypes have a crucial role in assigning final radiological outcomes in patients with COVID-19 pneumonia and differentiating between post-COVID-19 lung fibrosis and post-COVID-19 lung sequelae. RLAs, categorized as resolving, persistent, and progressive, provide a novel radiological classification of post-COVID-19 lung sequelae. Final phenotypic classification may play an important role in further workups and treatment planning for RLAs; some patients with progressive phenotypes may have underlying rheumatological predispositions and many cases may have resolving phenotypes requiring no further treatment, whereas cases with persistent RLAs require further follow-up.

Efficacy and Safety of Hydrogen Therapy in Patients with Early-Stage Interstitial Lung Disease: A Single-Center, Randomized, Parallel-Group Controlled Trial.

Several in vivo experiments have shown that molecular hydrogen is a promising therapeutic agent for interstitial lung diseases (ILD). In this study, hydrogen therapy was investigated to determine whether it is superior to N-Acetylcysteine (NAC) for the treatment of patients with early-stage ILD. A prospective, single-center, randomized, controlled clinical trial was conducted in 87 patients with early-stage ILD. Hydrogen or NAC therapy was randomly assigned (1:1 ratio) to the eligible patients. The primary endpoint was the change in the high-resolution computed tomography (HRCT) and composite physiologic index (CPI) scores from baseline to week 48. Pulmonary function was evaluated as a secondary endpoint, and adverse events were recorded for safety analysis. The rate of HRCT image improvement from the baseline in the HW group (63.6%) was higher than that in the NAC group (39.5%). A significant decrease in CPI and improvement in DLCO-sb were observed in the hydrogen group compared with those in the control group. Changes in other pulmonary function parameters, including FVC, FEV1, FEV1/FVC%, and TLC, were not significantly different between the two groups. Adverse events were reported in 7 (15.9%) patients in the HW group and 10 (23.3%) patients in the NAC group, but the difference was not significant (P=0.706). Hydrogen therapy exhibits superior efficacy and acceptable safety compared with NAC therapy in patients with early-stage ILD.

Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.

Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation. The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model's performance was evaluated using accuracy, precision, and recall as metrics. The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model. The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

OGBN P06 Co-location of pulmonary abnormalities on high resolution CT imaging and pulmonary microaspiration reflux scan: Suggests a causative relationship

Abstract Background The relationship between micro-aspiration of refluxate as a form of gastro-oesophageal reflux disease (GORD) and pulmonary diseases including bronchiectasis and lung fibrosis has been circumstantially suggested but not proven. Clarification of the role of GORD and microaspiration in pulmonary disease may enable targeted therapies. This study aims to determine whether there is a relationship between anatomical location of aspiration of radiological tracer on scintigraphic imaging and changes associated with pulmonary disease on high resolution computed tomographic imaging of the lung (HRCT-L). Methods Data were obtained from a prospectively populated database of patients with treatment resistant typical and atypical GORD and suspected reflux pulmonary microaspiration. Patients who underwent microaspiration studies and had concurrent HRCT-L for chest symptoms were included. Modified novel digital technique of reflux pulmonary microaspiration scintigraphy using a previously validated technique was performed to evaluate the possibility of significant GORD or extra-oesophageal symptoms, including typical symptoms such as heartburn and reflux and atypical symptoms such as throat symptoms, cough, globus, mucous, throat clearing, dysphonia and recurrent respiratory infections. HRCT-L was obtained in clinically indicated patients and compared with scintigraphic findings. Results Pulmonary reflux microaspiration scanning was performed in 121 patients (75% female, 25% male). The mean age was 67 years (range: 31 – 91 years). All patients demonstrated reflux microaspiration on scintigraphy in the supine delayed phase and all had intermittent or continuous full column gastro-oesophageal reflux. Lung parenchymal changes were apparent in 66 of 73 patients who underwent HRCT-L. Fibrotic changes were demonstrated in 51% of patients and bronchiectactic changes in 46%. Correlation at anatomical lobar levels with areas of aspiration of isotope on scintigraphic imaging was seen in 97% of fibrosis patients and 85% of bronchiectasis patients. Conclusions Modified reflux pulmonary microaspiration scintigraphy demonstrated the presence of pulmonary aspiration on delayed imaging. This is the first imaging technique with the ability to confirm the presence of lung aspiration of gastric content. Given the large proportion of cases showing lobar co-location of pulmonary abnormality on HRCT-L, this study indicates a strong relationship between aspiration of refluxate and parenchymal lung changes associated with pulmonary diseases such as IPF and bronchiectasis. There may be a significant role for modified technique scintigraphy to accurately diagnose the presence of reflux aspiration, allowing further research into pathogenesis and management of pulmonary disease.

Correlation of Impulse Oscillometry with Spirometry in Deployed Military Personnel with Airway Obstruction.

Evaluation of chronic respiratory symptoms in deployed military personnel has been conducted at Brooke Army Medical Center as part of the Study of Active Duty Military for Pulmonary Disease Related to Environmental Deployment Exposures III study. Although asthma and airway hyperreactivity have been the most common diagnoses, the clinical findings in these patients may be multifactorial. This study aims to evaluate the utility of impulse oscillometry (IOS) in diagnosing airway obstruction in patients undergoing multiple pulmonary function testing (PFT) studies. Military personnel referred for deployed-related pulmonary symptoms underwent a standardized evaluation at Brooke Army Medical Center and Walter Reed National Military Medical Center over a 5-year span. Initial studies included laboratory tests, high-resolution computed tomography imaging, cardiac evaluation with electrocardiogram, and echocardiography. PFT consisted of full PFTs, forced inspiratory/expiratory pressures, post-spirometry bronchodilator testing, IOS, exhaled nitric oxide, and methacholine challenge testing. A total of 360 patients have completed an evaluation to date. In this cohort, 108 patients (30.0%) have evidence of obstruction by spirometry, whereas 74 (20.6%) had IOS values of both an R5 > 150% and X5 < -1.5. Only 32 (8.9%) had evidence of obstruction by both spirometry and IOS, whereas 210 (57.3%) had neither. A comparison among R5 (resistance at 5 Hz), R20 (resistance at 20 Hz), and X5 (reactance at 5 Hz) was performed in those individuals with and without spirometric obstruction. R5 (% predicted) was 156.2 ± 57.4% (obstruction) vs. 129.1 ± 39.6% (no obstruction) (P < .001); R20 (% predicted) was 138.1 ± 37.7% (obstruction) vs. 125.3 ± 31.2% (no obstruction) (P = .007); and X5 (cmH2O/L/s) was -1.62 ± 1.28 (obstruction) vs. -1.25 ± 0.55 (no obstruction) (P < .001). Impulse oscillometry has been advocated as a supplemental pulmonary function test to aid in the diagnosis of airway obstruction. The use of IOS has been primarily used in pediatrics and elderly populations as a validated tool to establish a diagnosis of airway obstruction but is limited in the adult population because of a well-validated set of reference values. Prior studies in adults have most often demonstrated a correlation with an elevated R5 > 150%, elevated resonant frequency, and a negative X5 < -1.5 or a decrease of 30 to 35% in R5 post-bronchodilator. Impulse oscillometry may serve as an adjunct to diagnosis but likely cannot replace a standard spirometric evaluation. Our study highlights the future utility for diagnosing early obstructive disease in the symptomatic individual.

Wave In in auditory brainstem response suggests a high possibility of a high jugular bulb.

Wave In, which refers to the negativity between waves I and II in auditory brainstem response (ABR), is an electrophysiological phenomenon observed in previous studies. The term "high jugular bulb" (HJB) describes a jugular bulb that is located in a high position in the posterior aspect of the internal acoustic canal. The present study aimed to explore the correlation between wave In and the possibility of a HJB. This retrospective study included a cohort of pediatric patients diagnosed with profound hearing loss who were enrolled in a government-sponsored cochlear implantation program at an academic medical center between January 2019 and December 2022. The analysis involved examining the results obtained from the ABR test and high-resolution computed tomography (HRCT) of the temporal bone in the patients. The position of the jugular bulb was classified according to the Manjila and Semaan classification. A total of 221 pediatric patients were included in the study. Twenty-four patients, with a median age of 3 years and a range of 1-7 years, showed significant bilateral (n = 21) or unilateral (n = 3) wave In (mean latency: right ear, 2.16 ms ± 0.22 ms; left ear, 2.20 ms ± 0.22 ms). The remaining 197 patients showed an absence of ABR. The HRCT images revealed that 18 of the 24 patients (75%) had HJB, but only 41 of the 197 patients who lacked ABR (20.8%) showed signs of HJB. The ratio difference was considered statistically significant based on the chi-squared test (χ2 = 32.10, p < 0.01). More than 50% of the HJBs were categorized as type 4 jugular bulbs, which are located above the inferior margin of the internal auditory canal. ABR wave In in pediatric patients with profound hearing loss suggests a high possibility of HJB. The physiological mechanism underlying this correlation needs further investigation.

HRCT imaging of pulmonary involvement in granulomatosis with polyangiitis and microscopic polyangiitis at disease onset and during follow-up

BackgroundThe pulmonary involvement in patients with microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA) is well known at disease onset but data during follow-up (after the induction regimen and when the first relapse occurs) are limited. Our goal was to analyze chest high-resolution computed tomography (HRCT) findings of (ANCA)-associated vasculitis patients. MethodAll consecutive unselected AAV patients over eighteen with positive ANCA status and with HRCT chest performed at the diagnosis were retrospectively enrolled between 2004 and 2019 at the Toulouse University Hospital (France). Two experienced pulmonologists and one expert respiratory radiologist reviewed independently HRCT chest scans. ResultsA total of 157 AAV patients were included in the study. Two-thirds of AAV patients had pulmonary involvement at diagnosis. Diffuse alveolar hemorrhage (DAH) was observed in 31.2 % of cases, nodules and masses in 18.5 %, bronchial airway involvement in 13.4 %, and interstitial involvement in 12.7 %. Following the induction regimen, chest HRCT scans over a two-year period demonstrated significant improvement in DAH and nodular manifestations, whereas bronchial airway involvement exhibited variability and half of cases of interstitial lung disease (ILD) had progressive course. Outcomes and survival rates are better for nodular and bronchial involvement. DAH was the most frequent cause of deaths. Progressive fibrotic changes in ILD over time could impact prognosis despite AAV remission. ConclusionEmploying a pattern-based approach with HRCT chest scans to assess lung involvement could be valuable in predicting treatment response, relapse, mortality, and could improved the management of AAV patients.

Improving Kidney Tumor Detection Accuracy Using Hybrid U-Net Segmentation

Kidney cancer stands as a significant factor in cancer-related mortality, highlighting the critical importance of early and precise tumor detection This study introduces a computer-aided approach using the KiTS19 dataset and a hybrid U-Net architecture. Manual tumor segmentation is resource-intensive and prone to errors. Leveraging the hybrid U-Net, known for its proficiency in medical image analysis, we achieve precise tumor identification. Our method involves initial kidney and tumor segmentation in high-resolution CT images, followed by region of interest (ROI) generation and benign/malignant tumor classification. The assessment conducted on the KiTS19 dataset demonstrates encouraging outcomes, with Dice coefficients of 0.974 for kidney segmentation and 0.818 for tumor segmentation, accompanied by a tumor classification accuracy rate of 94.3%.The hybrid U-Net’s advanced feature extraction and spatial context awareness contribute to these outcomes. By streamlining diagnosis, our approach has the potential to significantly improve patient outcomes. The use of the KiTS19 dataset ensures robustness across various clinical cases and imaging modalities. This method represents a valuable advancement in computer-aided kidney tumor detection, promising to enhance patient care.

Reference Range of CT Value in NC-CBBCT Based on Female Breast Structure.

As a new high-resolution three-dimensional CT imaging technology, the essential reference range of CT values in Cone-beam breast computed tomography (CBBCT) has not been established to date. To determine the reference range of computed tomography (CT) values in CBBCT for clinical breast examination. In total, 913 cases (1167 lateral) were subject to CBBCT. CT values of the glandular tissue, fat and different quadrants and different distances of CBBCT images were analyzed. The nipple and muscle were also evaluated. A total of 672 lateral breasts were included in the normal group for investigation. The reference range of the absolute CT value of the chest wall muscle is -136.68~43.36 HU. The reference range of the absolute CT value of the nipple is 176.39~334.02 HU. The reference range of the absolute CT value of fat is -190.4~-63.67HU, and of glandular tissue is -12.2~199.07HU. Our results firstly established the baseline CT values of Non-contrast CBBCT in female breasts, which will benefit cancer screening and lesion locating. The closer the normal breast fat and glandular tissue is to the nipple, the greater the CT value. The older the age, the lower the density. The CT values of fat are unstable in a distance of less than 5 cm, and the CT values of glandular tissues are relatively stable. The difference between the upper and lower quadrants is significant in the same lateral breast and the same section.

Frequency of subclinical interstitial lung disease in COVID-19 autopsy cases: potential risk factors of severe pneumonia

Risk factors of severe coronavirus disease 2019 (COVID-19) have been previously reported; however, histological risk factors have not been defined thus far. The aim of this study was to clarify subclinical hidden interstitial lung disease (ILD) as a risk factor of severe pneumonia associated with COVID-19. We carefully examined autopsied lungs and chest computed tomography scanning (CT) images from patients with COVID-19 for interstitial lesions and then analyzed their relationship with disease severity. Among the autopsy series, subclinical ILD was found in 13/27 cases (48%) in the COVID-19 group, and in contrast, 8/65 (12%) in the control autopsy group (p = 0.0006; Fisher’s exact test). We reviewed CT images from the COVID-19 autopsy cases and verified that subclinical ILD was histologically detectable in the CT images. Then, we retrospectively examined CT images from another series of COVID-19 cases in the Yokohama, Japan area between February–August 2020 for interstitial lesions and analyzed the relationship to the severity of COVID-19 pneumonia. Interstitial lesion was more frequently found in the group with the moderate II/severe disease than in the moderate I/mild disease (severity was evaluated according to the COVID-19 severity classification system of the Ministry of Health, Labor, and Welfare [Japan]) (moderate II/severe, 11/15, 73.3% versus moderate I/mild, 108/245, 44.1%; Fisher exact test, p = 0.0333). In conclusion, it was suggested that subclinical ILD could be an important risk factor for severe COVID-19 pneumonia. A benefit of these findings could be the development of a risk assessment system using high resolution CT images for fatal COVID-19 pneumonia.