High-resolution Computed Tomography Images Research Articles

Generation and applications of synthetic computed tomography images for neurosurgical planning.

CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration. A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion. The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Clinical features and risk factors for primary Sjögren’s syndrome combined with interstitial lung disease: a retrospective study

Objective:To analyze the clinical characteristics of primary Sjögren’s syndrome (pSS) combined with interstitial lung disease (ILD), so as to provide a theoretical basis for the early diagnosis, treatment and prevention of PSS-ILD.Methods:From October 2017 to January 2022, patients with pSS who were admitted to the Department of Rheumatology at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine were included in this retrospective study. Patients were divided into the pSS-ILD (102 cases) and pSS-non-ILD groups (154 cases) based on the presence or absence of ILD on high-resolution computed tomography (HRCT). Demographics information, clinical symptoms, laboratory indicators and HRCT features were compared, and the logistic regression analysis was utilized to identify the risk factors.Results:A total of 256 patients were included. Patients with pSS-ILD were more often female, and their age and disease duration were significantly higher than those in the pSS-non-ILD group (p < 0.05). The HRCT imaging classification included ground glass-like shadow (78.4%) and patchy solid shadow (17.6%), and Non-specific interstitial pneumonitis (NSIP) (72.5%) was the predominant typology. Regarding the laboratory indexes, the positive rates of erythrocyte sedimentation rate, C-reactive protein, white blood cell count, neutrophil/lymphocyte ratio, triglycerides, total cholesterol, and anti-SS-A52 antibodies were significantly higher in the pSS-ILD patients than in the pSS-non-ILD group, while the positive rates of anti-synaptic antibodies were lower than in the pSS-non-ILD group, and the differences between two groups were statistically significant (p < 0.05). Logistic regression showed that age >60 years, longer duration of disease, higher triglycerides, and cholesterol were risk factors for pSS-ILD patients.Conclusion:The clinical features of pSS-ILD patients were xerophthalmia, cough and shortness of breath, and HRCT can help to diagnose the disease at an early stage. Age over 60 years, chronic course of disease, and elevated lipid levels are risk factors for ILD in pSS patients, and the relationship between autoimmune antibody levels and the occurrence of ILD needs to be further confirmed in follow-up studies with large sample sizes. These findings have the potential to provide useful information for early diagnosis, treatment, and prevention of the development of pSS-ILD.

Clinical and radiological correlation of pulmonary manifestations in rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a chronic inflammatory arthritis with various extra-articular manifestations, pulmonary being the most involved. Aims and Objectives: This study aims to identify the radiological patterns in the lungs among patients with RA. Materials and Methods: A total of 39 patients with RA were chosen randomly. They were examined and imaged by chest X-ray and high-resolution computed tomography (HRCT) thorax, and findings were analyzed. Results: The mean age of patients was 53.9 and had a female preponderance of 74.4%. Among the 39 patients studied, 30 had no respiratory symptoms. The most common HRCT thorax findings were bronchiectasis and ground-glass opacities. Conclusion: Radiological pulmonary manifestations of RA identified in HRCT imaging are wide and occur in the majority of clinically asymptomatic patients.

Assessing the feasibility of the transmastoid infralabyrinthine approach without decompression of the jugular bulb to the extradural part of the petrous apex and petroclival junction prior to surgery

Background and objectiveThis study aims to define specific measurements on cranial high-resolution computed tomography (HRCT) images prior to surgery to prove the feasibility of the navigated transmastoid infralabyrinthine approach (TI-A) without rerouting of the facial nerve (FN) and decompression of the jugular bulb (JB) in accessing the extradural-intrapetrous part of petrous bone lesions located at the petrous apex and petroclival junction.Materials and methodsVertical and horizontal distances of the infralabyrinthine space were measured on cranial HRCT images prior to dissection. Subsequently, the area of access was measured on dissected human cadaveric specimens. Infralabyrinthine access to the extradural part of the petrous apex and petroclival junction was evaluated on dissected specimens by two independent raters. Finally, the vertical and horizontal distances were correlated with the area of access.ResultsFourteen human cadaveric specimens were dissected bilaterally. In 54% of cases, the two independent raters determined appropriate access to the petrous apex and petroclival junction. A highly significant positive correlation (r = 0.99) was observed between the areas of access and the vertical distances. Vertical distances above 5.2 mm were considered to permit suitable infralabyrinthine access to the extradural area of the petrous apex and petroclival junction.ConclusionsPrior to surgery, vertical infralabyrinthine distances on HRCT images above 5.2 mm provide suitable infralabyrinthine access to lesions located extradurally at the petrous apex and petroclival junction via the TI-A without rerouting of the FN and without decompression of the JB.

Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction.

We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR). This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired t-test. Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR images (all comparisons, p ≤ 0.016). Depiction of the otic capsule, auditory ossicles, and tympanic membrane was significantly improved in DLR images compared with HIR images (both readers, p ≤ 0.003). Overall image quality was significantly superior in DLR images compared with HIR images (both readers, p < 0.001). Compared with HIR, DLR provided significantly better-quality high-resolution CT images of the temporal bone.

TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size

Background and ObjectivesSpread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals. MethodsFirst, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics. ResultsOur TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data. ConclusionsThe method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

Time-lapse 3D micro-tomography of calcite column experiments to study pH-dependent dynamic dissolution processes

We investigated the spatiotemporal evolution of calcite dissolution processes using time-lapse high-resolution X-ray CT imaging of live column experiments and real-time data logging of effluent pH and TDS. We evaluated the sensitivity of three pH solutions (7, 5, and 4) to dissolution processes. The results showed spatial variations in dissolution associated with spatial variation in porosity and pore sizes, manifesting as spatial heterogeneity in the dissolution pattern. Dissolution was dominant in larger pores compared to smaller ones. The magnitude of this uneven spatial dissolution exceeded the overall dissolution. However, the total porosity increased linearly over the time of dissolution experiments, with the slope dependent on the pH of the injected solution. Surface area decreased linearly during dissolution, and the decrease in surface area was also found to depend linearly on the increase in total porosity. The temporal evolution of pH-dependent reaction rates demonstrated a decreasing trend that followed a power-law behavior. The time to reach the steady reaction rate, which was evaluated from changes in calcite volume, appeared to be significantly later than the time at which the tracer reached its asymptote. The reaction rate at these two distinct times could differ by an order of magnitude. We emphasize the use of the steady state in mineral volume changes method to determine representative reaction rates in porous media, the significance of which emerges due to the coupling of hydrodynamic and reactive transport processes originating at the pore scale.

Does CISS MRI Reliably Depict the Endolymphatic Duct in Children with and without Vestibular Aqueduct Enlargement?

High-resolution CT is the mainstay for diagnosing an enlarged vestibular aqueduct (EVA), but MR imaging may be an appealing alternative, given its lack of ionizing radiation exposure. The purpose of this study was to determine how reliably MR imaging demonstrates the endolymphatic duct and endolymphatic duct enlargement in hearing-impaired children. We performed a retrospective review of temporal bone high-resolution CT and MR imaging of hearing-impaired children evaluated between 2017 and 2020. Vestibular aqueduct diameter was measured on high-resolution CT. The vestibular aqueducts were categorized as being enlarged (EVA+) or nonenlarged (EVA-) using the Cincinnati criteria. The endolymphatic ducts were assessed on axial high-resolution CISS MR imaging. We categorized endolymphatic duct visibility into the following: type 1 (not visible), type 2 (faintly visible), and type 3 (easily visible). Mixed-effect logistic regression was used to identify associations between endolymphatic duct visibility and EVA. Interreader agreement for the endolymphatic duct among 3 independent readers was assessed using the Fleiss κ statistic. In 196 ears from 98 children, endolymphatic duct visibility on MR imaging was type 1 in 74.0%, type 2 in 14.8%, and type 3 in 11.2%; 20.4% of ears were EVA+ on high-resolution CT. There was a significant association between EVA+ status and endolymphatic duct visibility (P < .01). Endolymphatic duct visibility was type 1 in 87.1%, type 2 in 12.8%, and type 3 in 0% of EVA- ears and type 1 in 22.5%, type 2 in 22.5%, and type 3 in 55.0% of EVA+ ears. The predicted probability of a type 3 endolymphatic duct being EVA+ was 0.997. There was almost perfect agreement among the 3 readers for distinguishing type 3 from type 1 or 2 endolymphatic ducts. CISS MR imaging substantially underdiagnoses EVA; however, when a type 3 endolymphatic duct is evident, there is a >99% likelihood of an EVA.

Deep learning-based harmonization of trabecular bone microstructures between high- and low-resolution CT imaging.

Osteoporosis is a bone disease related to increased bone loss and fracture-risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial-resolution and other imaging features among different CT scanners add inconsistency to derived bone microstructural metrics, urging the need for harmonization of image data from different scanners. This paper presents a new deep learning (DL) method for the harmonization of bone microstructural images derived from low- and high-resolution CT scanners and evaluates the method's performance at the levels of image data as well as derived microstructural metrics. We generalized a three-dimensional (3D) version of GAN-CIRCLE that applies two generative adversarial networks (GANs) constrained by the identical, residual, and cycle learning ensemble (CIRCLE). Two GAN modules simultaneously learn to map low-resolution CT (LRCT) to high-resolution CT (HRCT) and vice versa. Twenty volunteers were recruited. LRCT and HRCT scans of the distal tibia of their left legs were acquired. Five-hundred pairs of LRCT and HRCT image blocks of voxels were sampled for each of the twelve volunteers and used for training in supervised as well as unsupervised setups. LRCT and HRCT images of the remaining eight volunteers were used for evaluation. LRCT blocks were sampled at 32 voxel intervals in each coordinate direction and predicted HRCT blocks were stitched to generate a predicted HRCT image. Mean±standard deviation of structural similarity (SSIM) values between predicted and true HRCT using both 3DGAN-CIRCLE-based supervised (0.84±0.03) and unsupervised (0.83±0.04) methods were significantly (p<0.001) higher than the mean SSIM value between LRCT and true HRCT (0.75±0.03). All Tb measures derived from predicted HRCT by the supervised 3DGAN-CIRCLE showed higher agreement (CCC [0.956 0.991]) with the reference values from true HRCT as compared to LRCT-derived values (CCC [0.732 0.989]). For all Tb measures, except Tb plate-width (CCC=0.866), the unsupervised 3DGAN-CIRCLE showed high agreement (CCC [0.920 0.964]) with the true HRCT-derived reference measures. Moreover, Bland-Altman plots showed that supervised 3DGAN-CIRCLE predicted HRCT reduces bias and variability in residual values of different Tb measures as compared to LRCT and unsupervised 3DGAN-CIRCLE predicted HRCT. The supervised 3DGAN-CIRCLE method produced significantly improved performance (p<0.001) for all Tb measures as compared to the two DL-based supervised methods available in the literature. 3DGAN-CIRCLE, trained in either unsupervised or supervised fashion, generates HRCT images with high structural similarity to the reference true HRCT images. The supervised 3DGAN-CIRCLE improves agreements of computed Tb microstructural measures with their reference values and outperforms the unsupervised 3DGAN-CIRCLE. 3DGAN-CIRCLE offers a viable DL solution to retrospectively improve image resolution, which may aid in data harmonization in multi-site longitudinal studies where scanner mismatch is unavoidable.

Normative reference data for intracranial volume in children: The results of CT volumetry.

Intracranial volume (ICV) is an important indicator of the development of the brain and skull in children. At present, there is a lack of ICV growth standards based on large infant and children samples. Our aim was to assess the normal range of the ICV variation in Russian children using a modern automatic system for constructing the endocranial cavity (Endex) and to provide growth standards of the ICV for clinical practice. High-resolution head CT scans were obtained from 673 apparently healthy children (380 boys and 293 girls) aged 0-17years and transformed into the ICV estimates using the Endex software. The open-source software RefCurv utilizing R and the GAMLSS add-on package with the LMS method was then used for the construction of smooth centile growth references for ICV according to age and sex. We demonstrated that the ICVs estimates calculated using the Endex software are perfectly comparable with those obtained by a conventional technique (i.e. seed feeling). Sex-specific pediatric growth charts for ICV were constructed. This study makes available for use in clinical practice ICV growth charts for the age from 0 to 17 based on a sample of 673 high-resolution CT images.

Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.

Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT. Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe. ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils. Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

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.

Images in Vascular Medicine: High-resolution CT imaging of arterial calcification in the hands and legs of patients with CD73 deficiency.

Images in Vascular Medicine: High-resolution CT imaging of arterial calcification in the hands and legs of patients with CD73 deficiency.

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.