High-resolution Computed Tomography Data Research Articles

Dynamic parallel traction theoretical model for the application and validation in femoral neck fractures - a finite element analysis

Study objectiveThis study aims to validate the application effects of a novel theoretical model of dynamic parallel traction in the treatment of femoral neck fractures through three-dimensional finite element analysis. By simulating the femoral neck fracture model, we explore the promotional effect of dynamic parallel traction on fracture healing. MethodA digital 3D femur model was constructed using high-resolution computed tomography data of the lower limbs of a 70-year-old elderly subject. An axial compression of 500N was applied at different traction angles (0°, 10°, 20°, 30°, 40°, 50°). The equivalent stress distribution and deformation of the femur geometric model were calculated at each angle under the six α angles. Statistical analysis was performed using One-Way ANOVA. ResultsAt the parallel angle (α = 0°), the maximum stress on the entire femur occurred at the trochanteric fossa, with a value of 7.945 MPa (α = 0°). The maximum deformation was at the fovea capitis, with a value of 104.13 mm (α = 0°). As the traction angle gradually increased (α = 10°, α = 20°, α = 30°, α = 40°, α = 50°), the maximum stress shifted gradually to the medial cortex of the femoral shaft, with values of 11.236 MPa (α = 10°), 15.196 MPa (α = 20°), 19.263 MPa (α = 30°), 23.149 MPa (α = 40°), and 26.311 MPa (α = 50°). The maximum deformation remained at the fovea capitis but increased to 131.87 mm (α = 10°), 181.96 mm (α = 20°), 228.2 mm (α = 30°), 271.15 mm (α = 40°), and 307.41 mm (α = 50°). One-Way ANOVA revealed that traction angle significantly influenced the stress distribution (F = 4.419, p = 0.0022) and deformation magnitude (F = 4.023, p = 0.0040) at the proximal femur, indicating that traction angle is a critical factor affecting stress distribution and deformation. ConclusionWith the increase of the traction angle, the mechanical properties of the proximal femur decrease, indicating an increased risk of non-union and complications. Additionally, the study proves the effectiveness of the “dynamic parallel traction” theory.

Preoperative Planning of Craniectomy and Reconstruction using 3D Printed Cranioplasty for Treatment of Calvarial Lesion

There is a number of diseases which causes calvarial lesion. Fibrous dysplasia, intraosseous meningioma, osteoma is to name a few. Treatment of these lesions necessitates removal of part of the skull or craniectomy to decompress the brain and neural elements. The ensuing skull defect needs to be repaired to protect the brain. Previously used allograft and alloplastic materials have been replaced with newer PEEK material, which is more resistant, biocompatible and can be 3D printed. In this series we describe 4 cases in which skull lesions are removed and reconstructed in the same sitting using 3D printed PEEK implant designed preoperatively using high resolution CT data. The results are superior and complication rates are low. All the cases were done in different government hospitals. The technical process was developed indigenously, and the methods are the first in our country. Bang. J Neurosurgery 2024; 13(2): 64-69

The skull of the semi-aquatic neotropical lizard Echinosaura horrida (Gymnophthalmidae: Cercosaurinae) reveals new synapomorphies within Gymnophthalmoidea.

The rough teiid or water cork lizard (Echinosaura horrida) is a small reptile from Colombia and Ecuador placed in a genus that contains eight species and well-known phylogenetic relationships. Here we provide a detailed description and illustrations, bone by bone, of its skull, while we discussed its intraspecific variation by comparing high-resolution computed tomography data from two specimens and the variation within the genus by including previously published data from Echinosaura fischerorum. This allowed to propose putative diagnostic character states for Echinosaura horrida and synapomorphies for Echinosaura. In addition, our discussion includes broader comparisons of new character transformations of the jugal, vomer, orbitosphenoid, and hyoid. These characters are important for diagnosing clades at different levels of the Gymnophthalmoidea phylogeny.

Silent hypoxemia – A specific sign of COVID-associated pneumonia?

Introduction. Pneumonia is a frequent manifestation of coronavirus infection. COVID-associated pneumonia is a disease characterized by a non-standard course and a number of clinical phenomena that complicate timely diagnosis and treatment.Aim. To investigate the phenomenon of mute hypoxemia in COVID-associated pneumonia.Materials and methods. The study included 214 patients who were divided into 2 groups. The study group included patients with confirmed COVID-associated pneumonia, and the control group included patients with interstitial lung diseases (idiopathic pulmonary fibrosis, nonspecific interstitial pneumonia, hypersensitivity pneumonitis). The subjective condition of the patient, presence of concomitant pathology, high-resolution computed tomography data, arterial blood gas composition, and spirometry data were evaluated.Results. In patients with COVID-associated pneumonia, “silent hypoxemia” was encountered 1.3 times more frequently than in patients with non-COVID-associated pneumonia. When comparing patients with silent hypoxemia and hypoxemia with dyspnea in COVID-associated pneumonia, statistically significantly higher values of PaCO2 and lower values of respiratory rate are observed. Such patterns are not detected in non-COVID-associated pneumonia. In patients with silent hypoxemia in non-COVID-associated pneumonia, the respiratory rate is statistically significantly higher compared to patients with COVID-associated pneumonia. Univariate logistic regression analysis demonstrates that in patients with non-COVID-associated pneumonia, silent hypoxemia is associated with BMI increase (OR = 1.380 (95% CI: 1.058–1.801); p = 0.017).Conclusion. The phenomenon of “silent hypoxemia” may manifest not only in pulmonary impairments resulting from SARS-CoV-2 infection but notably in COVID-associated pneumonia, where the absence of patient-reported dyspnea is substantiated by the lack of tachypnea. Owing to the subtleties of “silent hypoxemia”, clinical presentations may exhibit delays, diverting attention from significant pulmonary compromise, which could subsequently precipitate the failure of compensatory mechanisms.

Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach

PurposeRadiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data. MethodsOur study included 132 thoracic CT scans from clinical practice, annotated by 13 radiologists. In three iterative training experiments, we aimed to improve and accelerate segmentation of the heart and mediastinum. Each experiment started with manual segmentation of 5–25 CT scans, which served as training data for a nnU-Net. Further iterations incorporated AI pre-segmentation and human correction to improve accuracy, accelerate the annotation process, and reduce human involvement over time. ResultsResults showed consistent improvement in AI model quality with each iteration. Resampled datasets improved the Dice similarity coefficients for both the heart (DCS 0.91 [0.88; 0.92]) and the mediastinum (DCS 0.95 [0.94; 0.95]). Our AI models reduced human interaction time by 50 % for heart and 70 % for mediastinum segmentation in the most potent iteration. A model trained on only five datasets achieved satisfactory results (DCS > 0.90). ConclusionsThe iterative training workflow provides an efficient method for training AI-based segmentation models in multi-center studies, improving accuracy over time and simultaneously reducing human intervention. Future work will explore the use of fewer initial datasets and additional pre-processing methods to enhance model quality.

Heterogeneity of determining disease severity, clinical course and outcomes in systemic sclerosis-associated interstitial lung disease: a systematic literature review

Objective The course of systemic sclerosis-associated interstitial lung disease (SSc-ILD) is highly variable and different from continuously progressive idiopathic pulmonary fibrosis (IPF). Most proposed definitions of progressive pulmonary fibrosis or...

Comparing linear and non-linear models to estimate the appropriate cochlear implant electrode array length—are current methods precise enough?

PurposeIn cochlear implantation with flexible lateral wall electrode arrays, a cochlear coverage (CC) range between 70% and 80% is considered ideal for optimal speech perception. To achieve this CC, the cochlear implant (CI) electrode array has to be chosen according to the individual cochlear duct length (CDL). Here, we mathematically analyzed the suitability of different flexible lateral wall electrode array lengths covering between 70% and 80% of the CDL.MethodsIn a retrospective cross-sectional study preoperative high-resolution computed tomography (HRCT) from patients undergoing cochlear implantation was investigated. The CDL was estimated using an otosurgical planning software and the CI electrode array lengths covering 70–80% of the CDL was calculated using (i) linear and (ii) non-linear models.ResultsThe analysis of 120 HRCT data sets showed significantly different model-dependent CDL. Significant differences between the CC of 70% assessed from linear and non-linear models (mean difference: 2.5 mm, p < 0.001) and the CC of 80% assessed from linear and non-linear models (mean difference: 1.5 mm, p < 0.001) were found. In up to 25% of the patients none of the existing flexible lateral wall electrode arrays fit into this range. In 59 cases (49,2%) the models did not agree on the suitable electrode arrays.ConclusionsThe CC varies depending on the underlying CDL approximation, which critically influences electrode array choice. Based on the literature, we hypothesize that the non-linear method systematically overestimates the CC and may lead to rather too short electrode array choices. Future studies need to assess the accuracy of the individual mathematical models.

Radiological feature heterogeneity supports etiological diversity among patient groups in Meniere’s disease

We aimed to determine the prevalence of radiological temporal bone features that in previous studies showed only a weak or an inconsistent association with the clinical diagnosis of Meniere’s disease (MD), in two groups of MD patients (n = 71) with previously established distinct endolymphatic sac pathologies; i.e. the group MD-dg (ES degeneration) and the group MD-hp (ES hypoplasia). Delayed gadolinium-enhanced MRI and high-resolution CT data were used to determine and compare between and within (affected vs. non-affected side) groups geometric temporal bone features (lengths, widths, contours), air cell tract volume, height of the jugular bulb, sigmoid sinus width, and MRI signal intensity alterations of the ES. Temporal bone features with significant intergroup differences were the retrolabyrinthine bone thickness (1.04 ± 0.69 mm, MD-hp; 3.1 ± 1.9 mm, MD-dg; p < 0.0001); posterior contour tortuosity (mean arch-to-chord ratio 1.019 ± 0.013, MD-hp; 1.096 ± 0.038, MD-dg; p < 0.0001); and the pneumatized volume (1.37 [0.86] cm3, MD-hp; 5.25 [3.45] cm3, MD-dg; p = 0.03). Features with differences between the affected and non-affected sides within the MD-dg group were the sigmoid sinus width (6.5 ± 1.7 mm, affected; 7.6 ± 2.1 mm, non-affected; p = 0.04) and the MRI signal intensity of the endolymphatic sac (median signal intensity, affected vs. unaffected side, 0.59 [IQR 0.31–0.89]). Radiological temporal bone features known to be only weakly or inconsistently associated with the clinical diagnosis MD, are highly prevalent in either of two MD patient groups. These results support the existence of diverse—developmental and degenerative—disease etiologies manifesting with distinct radiological temporal bone abnormalities.

Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges.

The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.

High-Resolution Computed Tomography as an Initial Diagnostic and Localization Tool in Patients with Cerebrospinal Fluid Rhinorrhea: A Meta-Analysis

Background and Objectives: This study was performed to investigate the utility of high-resolution computed tomography (HRCT) for the initial localization of cerebrospinal fluid rhinorrhea. Methods: HRCT data regarding the point of cerebrospinal fluid leakage (as confirmed in the operating room), collected up to December 2022, were extracted from five databases. The risk of bias of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results: The search revealed eight relevant studies with a total of 254 patients. The diagnostic odds ratio of the imaging studies was 10.0729 (95% confidence interval [CI]: 2.4486; 41.4376; I2 = 54.1%). The area under the summary receiver operating characteristic curve was 0.8. Sensitivity, specificity, the negative predictive value, and the positive predictive value were 0.7550 (95% CI: 0.6163; 0.8553; I2 = 69.8%), 0.8502 (95% CI: 0.5986; 0.9557, I2 = 49.3%), 0.4106 (95% CI: 0.2418; 0.6035; I2 = 59.0%), and 0.9575 (95% CI: 0.8955; 0.9834; I2 = 27.7%), respectively. Conclusions: HRCT can be used to accurately localize cerebrospinal fluid rhinorrhea because it shows bony defects in high detail. However, it has limited utility for the evaluation of active leakage, and localization is difficult in the presence of coexisting lesions.

Assessment of the distribution and volume of air chambers around the inner auditory canal on high-resolution computed tomography scans of the temporal bone.

Assessment of the distribution and volume of air chambers around the inner auditory canal on high-resolution computed tomography scans of the temporal bone.

Lung Ultrasound in Children With Systemic Juvenile Idiopathic Arthritis-Associated Interstitial Lung Disease.

Systemic juvenile idiopathic arthritis (JIA) associated with lung disease (JIA-LD) is a potentially life threating complication in children with systemic JIA. Although high-resolution computed tomography (HRCT) is considered the gold standard imaging modality for evaluating interstitial lung disease (ILD), lung ultrasound (US) has shown utility for ILD screening in adults with connective tissue diseases at lower cost and without using ionizing radiation. The goals of this pilot study were to describe lung US features in children with known systemic JIA-LD and to assess the feasibility of lung US in this population. Children age <18 years with systemic JIA-LD and healthy controls were enrolled. Lung US acquisition was performed at 14 lung positions. Demographic, clinical, and HRCT data were collected and reviewed. Feasibility was assessed through patient surveys. Lung US findings were qualitatively and semiquantitatively assessed and compared to HRCT findings. Lung US was performed in 9 children with systemic JIA-LD and 6 healthy controls and took 12 minutes on average to perform. Lung US findings in systemic JIA-LD included focal to diffuse pleural irregularity, granularity, and thickening, with associated scattered or coalesced B-lines, and subpleural consolidations. Lung US findings appeared to correspond to HRCT findings. Lung US in systemic JIA-LD reveals highly conspicuous abnormalities in the pleura and subpleura that appear to correlate with peripheral lung findings on HRCT. Lung US is a feasible imaging tool in children even from an early age. This study suggests a potential role of lung US in systemic JIA-LD screening, diagnosis, and/or prognostication.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues.

Volumetry improves the assessment of the vestibular aqueduct size in inner ear malformation

ObjectivesEnlarged vestibular aqueduct (EVA) is a common finding associated with inner ear malformations (IEM). However, uniform radiologic definitions for EVA are missing and various 2D-measurement methods to define EVA have been reported. This study evaluates VA volume in different types of IEM and compares 3D-reconstructed VA volume to 2D-measurements.MethodsA total of 98 high-resolution CT (HRCT) data sets from temporal bones were analyzed (56 with IEM; [cochlear hypoplasia (CH; n = 18), incomplete partition type I (IPI; n = 12) and type II (IPII; n = 11) and EVA (n = 15)]; 42 controls). VA diameter was measured in axial images. VA volume was analyzed by software-based, semi-automatic segmentation and 3D-reconstruction. Differences in VA volume between the groups and associations between VA volume and VA diameter were assessed. Inter-rater-reliability (IRR) was assessed using the intra-class-correlation-coefficient (ICC).ResultsLarger VA volumes were found in IEM compared to controls. Significant differences in VA volume between patients with EVA and controls (p < 0.001) as well as between IPII and controls (p < 0.001) were found. VA diameter at the midpoint (VA midpoint) and at the operculum (VA operculum) correlated to VA volume in IPI (VA midpoint: r = 0.78, VA operculum: r = 0.91), in CH (VA midpoint: r = 0.59, VA operculum: r = 0.61), in EVA (VA midpoint: r = 0.55, VA operculum: r = 0.66) and in controls (VA midpoint: r = 0.36, VA operculum: r = 0.42). The highest IRR was found for VA volume (ICC = 0.90).ConclusionsThe VA diameter may be an insufficient estimate of VA volume, since (1) measurement of VA diameter does not reliably correlate with VA volume and (2) VA diameter shows a lower IRR than VA volume. 3D-reconstruction and VA volumetry may add information in diagnosing EVA in cases with or without additional IEM.

Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network.

Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) are the 2 most common chronic middle ear diseases. In the process of diagnosis and treatment, the 2 diseases are prone to misdiagnosis and missed diagnosis due to their similar clinical manifestations. High resolution computed tomography (HRCT) can clearly display the fine anatomical structure of the temporal bone, accurately reflect the middle ear lesions and the extent of the lesions, and has advantages in the differential diagnosis of chronic middle ear diseases. This study aims to develop a deep learning model for automatic information extraction and classification diagnosis of chronic middle ear diseases based on temporal bone HRCT image data to improve the classification and diagnosis efficiency of chronic middle ear diseases in clinical practice and reduce the occurrence of missed diagnosis and misdiagnosis. The clinical records and temporal bone HRCT imaging data for patients with chronic middle ear diseases hospitalized in the Department of Otorhinolaryngology, Xiangya Hospital from January 2018 to October 2020 were retrospectively collected. The patient's medical records were independently reviewed by 2 experienced otorhinolaryngologist and the final diagnosis was reached a consensus. A total of 499 patients (998 ears) were enrolled in this study. The 998 ears were divided into 3 groups: an MEC group (108 ears), a CSOM group (622 ears), and a normal group (268 ears). The Gaussian noise with different variances was used to amplify the samples of the dataset to offset the imbalance in the number of samples between groups. The sample size of the amplified experimental dataset was 1 806 ears. In the study, 75% (1 355) samples were randomly selected for training, 10% (180) samples for validation, and the remaining 15% (271) samples for testing and evaluating the model performance. The overall design for the model was a serial structure, and the deep learning model with 3 different functions was set up. The first model was the regional recommendation network algorithm, which searched the middle ear image from the whole HRCT image, and then cut and saved the image. The second model was image contrast convolutional neural network (CNN) based on twin network structure, which searched the images matching the key layers of HRCT images from the cut images, and constructed 3D data blocks. The third model was based on 3D-CNN operation, which was used for the final classification and diagnosis of the 3D data block construction, and gave the final prediction probability. The special level search network based on twin network structure showed an average AUC of 0.939 on 10 special levels. The overall accuracy of the classification network based on 3D-CNN was 96.5%, the overall recall rate was 96.4%, and the average AUC under the 3 classifications was 0.983. The recall rates of CSOM cases and MEC cases were 93.7% and 97.4%, respectively. In the subsequent comparison experiments, the average accuracy of some classical CNN was 79.3%, and the average recall rate was 87.6%. The precision rate and the recall rate of the deep learning network constructed in this study were about 17.2% and 8.8% higher than those of the common CNN. The deep learning network model proposed in this study can automatically extract 3D data blocks containing middle ear features from the HRCT image data of patients' temporal bone, which can reduce the overall size of the data while preserve the relationship between corresponding images, and further use 3D-CNN for classification and diagnosis of CSOM and MEC. The design of this model is well fitting to the continuous characteristics of HRCT data, and the experimental results show high precision and adaptability, which is better than the current common CNN methods.

The Importance of High-Resolution Computed Tomography in the Early Diagnosis of Fibrotic Hypersensitivity Pneumonitis

Objective: to determine the high-resolution computed tomography (HRCT) signs and their combinations, suggesting the presence of fibrotic hypersensitivity pneumonitis (FHP). Material and methods. The study included 52 patients with pathologically verified diagnosis of FHP who were examined according to the 2021 consensus criteria. All patients had lung HRCT no more than 4 months prior to lung biopsy. The analysis of the changes revealed during HRCT was carried out by qualitative and semi-quantitative methods. The presence of individual signs, the degree of their severity (as percentage relative to the volume of the entire lung tissue) and the features of distribution in the axial and longitudinal planes were taken into account. To search for significant combinations of HRCT signs, a correlation analysis was carried out. Results. The most common HRCT signs in the examined group of FHP patients were reticular changes (96 %) and ground glass opacity (88 %). Most of the patients had signs that did not correspond to the picture of “typical FHP”, such as ground glass and emphysema. Relatively rare cases were centrilobular lesions (46.2 %) and mosaic attenuation (52 %), which are traditionally part of the “typical FHP” picture. In 22 cases (42.3 %) there were signs of emphysema, the presence of which significantly complicated the interpretation of HRCT data even in the cases of characteristic signs of FHP. Correlations of almost all HRCT signs were weak. The most common combinations of features were ground glass + reticular changes, emphysema + honeycombing, reticular changes + bronchiectasis. The combination of ground glass and reticular changes had a moderate correlation, but it has low specificity. Conclusion. The HRCT picture of changes in the lungs among patients with a morphologically verified diagnosis of FHP has important features. A weak correlation between the signs does not allow to identify their combinations that can help in the early diagnosis of FHP with sufficient reliability.

In-silico optimisation of ICD defibrillation efficacy by modifying lead/can configurations using a cohort of high-resolution whole-torso heart models

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Medical Research Council, UK Background ICD is an effective direct therapy against VT/VF by applying a strong electrical shock across the heart between the shocking coil and can. Conventionally, patients will have a shocking coil inside the right ventricle (RV) and a can at the upper left chest. However, due to infections or other conditions, the can may need to place towards the right chest. The placement of the RV coil may also vary in different cases, for example avoiding scar. However, it is unclear how defibrillation efficacy may be altered by these unavoidable modifications to conventional lead/can configurations and whether optimisation may be possible. Purpose To compare defibrillation efficacy of modifications of ICD configurations in a cohort of whole-torso models. Methods A cohort of 15 whole torso models was generated from high resolution CT data and contrast CT cardiac scans, including 5 dilated cardiomyopathy (DCM), 5 hypertrophic cardiomyopathy (HCM) and 5 structurally normal patients (Fig A). Transvenous ICDs were represented by a shocking coil inside the RV (near apex) and a (ground) can at the upper left chest as default settings. Configurations were then varied by moving the can to the right chest, moving the RV coil up the mid-septum or adding extra grounds (Superior Vena Cava (SVC) coil, coronary sinus (CS) coil (Fig A)). Defibrillation-strength shocks were applied to all models (Fig B). DFTs and mean electrical field were evaluated across the whole heart as well as specific LV, RV, RV insertion regions, along with overall impedance. Results Shifting the can from left to right significantly increased DFT for the whole heart (23 J vs 15 J, P=0.03) and LV (25 J vs 17 J, P=0.03) (Fig C) and reduced the mean electrical field. Moving the RV coil further up the septum did not significantly alter DFT (Fig D), but did reduce mean electrical field for all regions and reduce impedance significantly. Additional separate coils significantly reduced DFT for all regions (Fig D) by increasing mean electrical field, whilst adding both coils significantly reduced DFT the most (whole heart: 15 J vs 6 J, P=0.03) (Fig E). Impedance was increased significantly by adding SVC coil, but reduced significantly by adding CS coil. Adding both coils increased impedance slightly. Conclusions Although a right-sided can increases DFT by over 50%, additional leads (grounds) may mitigate this increase by increasing mean electrical field. Moving the RV coil closer to the mid-septum reduces DFT slightly, but also reduces mean electrical field and impedance significantly.

Spectral propagation-based x-ray phase-contrast computed tomography.

Purpose: Propagation-based x-ray imaging (PBI) is a phase-contrast technique that is employed in high-resolution imaging by introducing some distance between sample and detector. PBI causes characteristic intensity fringes that have to be processed with appropriate phase-retrieval algorithms, which has historically been a difficult task for objects composed of several different materials. Spectral x-ray imaging has been introduced to PBI to overcome this issue and to potentially utilize the spectral nature of the data for material-specific imaging. We aim to explore the potential of spectral PBI in three-dimensional computed tomography (CT) imaging in this work. Approach: We demonstrate phase-retrieval for experimental high-resolution spectral propagation-based CT data of a simple two-component sample, as well as a multimaterial capacitor test sample. Phase-retrieval was performed using an algorithm based on the Alvarez-Macovski model. Virtual monochromatic (VMI) and effective atomic number images were calculated after phase-retrieval. Results: Phase-retrieval results from the spectral data set show a distinct gray-level for each material with no residual phase-contrast fringes. Several representations of the phase-retrieved data are provided. The VMI is used to display an attenuation-equivalent image at a chosen display energy of 80keV, to provide good separation of materials with minimal noise. The effective atomic number image shows the material composition of the sample. Conclusions: Spectral photon-counting detector technology has already been shown to be compatible with spectral PBI, and there is a foreseeable need for robust phase-retrieval in high-resolution, spectral x-ray CT in the future. Our results demonstrate the feasibility of phase-retrieval for spectral PBI CT.

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

AbstractInterstitial lung diseases (ILDs) are adverse disorders, damaging the lung tissues, thus making timely diagnosis imperative. To counter the scarcity of publicly available high‐resolution computed tomography (HRCT) data, architecture employing HRCT and x‐ray data have been proposed for diagnosing ILDs. A model is first trained to diagnose three ILDs and a healthy lung from x‐ray data and then with a small amount of HRCT data of the same four classes. We introduce an EfficientNet+AlexNet model and a custom deep convolutional neural network and compare their performances with different weight initialization at the start of x‐ray image training. The EfficientNet+AlexNet model initialized with ImageNet weights performed best on the x‐ray dataset and was later used for domain adaptation. We also compare our model's performance to a single pre‐trained EfficientNetB0 model (trained on the same HRCT data). Our model gave an accuracy of 97.4% on the testing dataset, a notable increase in the performance compared to the recently proposed methodologies. The experimental results and graphical plots depict the superiority of domain adaptation and the potentials of weights that are a starting point for training the models.

Measuring the density of the fissula antefenestram and the section of the basal turn of the cochlea: Are they useful in the radiological diagnosis of otosclerosis?

IntroductionThe role of objective parameters in terms of improvement of the accuracy of high-resolution computed tomography (HRCT) of the temporal bone in the diagnosis of otosclerosis remains unclear. ObjectivesTo investigate the relationship between the density of the fissula antefenestram (FAF) and of the width of the transversal section of the basal turn of the cochlea (BTC), and the diagnosis of otosclerosis. MethodsThis is a retrospective study in which preoperative HRCT data from ears of patients submitted to stapedotomy due to otosclerosis (case group) were evaluated. For the control group, normal hearing ears having undergone HRCT for other purposes were included. Case and control HRCT images were objectively assessed by an experienced blinded radiologist. During this evaluation, measurements of the relative radiological density of the FAF and of the transversal section of the BTC were obtained. The results were compared between the groups. Also, a receiver operating characteristic curve was created and the area under the curve (AUC) was calculated for each variable. Significance level was set at .05. Results40 ears were included in each group. Case ears presented reduced values for the relative radiological density on the FAF (p-value<0.0001). Moreover, ears with otosclerosis (p-value: 0.022) presented lower transversal section of the BTC. The AUC for these variables reached 0.929 and 0.646, respectively. ConclusionsOtosclerotic ears present reduced radiological density on the FAF and narrower BTC. The relative density of the FAF also shows a great diagnostic power in the context of this disease.