Radiographic Research Articles

Reliability and utility of the new Belt et al. classification for revision of infected total knee arthroplasty.

The frequency of revisions in total knee arthroplasty (TKA) is rising. Various classifications of bone defects exist, each with its own limitations. Recently, Belt et al. have proposed a new classification for TKA revisions based on X-ray imaging. We evaluated the Belt et al. classification and verified if this new classification is reliable, and if it correlates with the implant used during revision surgery for periprosthetic joint infection. This is a retrospective study. We reproduced the paper proposed by Belt et al. with the radiological data of all patients who underwent two stage revision for infected TKA in our institution between January 2017 and December 2022. Five different operators classified the bone defect for each patient at two time points. Subsequently, we assessed intra- and inter-operator reproducibility. We also collect the surgery data from our registry to verify if there is a correlation between augment use and epiphyseal bone defect. The classification proposed by Belt is reliable, and have a good reproducibility inter and intraoperator. There is no correlation between the bone defect. And the use of augment, and so this classification is usless in the prediction of the material needed in the operating room. The Belt at al. classification is reliable, but a classification which can predict the implant neded have to be developed.

Accuracy and precision of in vitro EOS imaging compared to digital radiographs in the measurement of intramedullary lengthening.

Accurate limb length measurement is essential during limb lengthening procedures to prevent neurologic and musculoskeletal complications. Magnetic intramedullary lengthening nails (MILN) rely on radiographs for measurement, but the optimal radiographic technique is not established. This study evaluated the precision and accuracy of EOS imaging versus digital radiography, including calibration techniques and measurement techniques for assessing intramedullary lengthening. An MILN was inserted into a Sawbones femur, and lengthening measurements from digital X-ray and EOS imaging were compared to external remote control (ERC) results. Measurements were taken with and without calibration using a magnification ball, nail width, or female nail length. Four observers measured the distraction gap, spindle length, full nail length, and male nail length at various hip flexion angles. Precision and accuracy were calculated for each technique. From 576 measurements, EOS imaging demonstrated significantly higher accuracy than digital radiography (83.6% vs. 73.5% absolute accuracy; p < 0.001) when non-calibrated values were included. EOS maintained superior accuracy at 30mm, 50mm, and 70mm of lengthening (p < 0.05), with no difference at 10mm. Calibration did not affect EOS accuracy, whereas digital radiography was less accurate without calibration (p < 0.001). Distraction gap measurement was the most accurate for both modalities, and 0° hip flexion provided the highest precision. Overall, EOS was more precise (79.4% vs. 71.7% precision rates; p < 0.001). EOS imaging is recommended for limb lengthening measurements due to its superior precision and accuracy. For digital radiographs, careful calibration is essential to achieve accurate measurements.

Muscle imaging in facioscapulohumeral muscular dystrophy research: A scoping review and expert recommendations.

Clinical trial readiness is an important topic in the field of facioscapulohumeral muscular dystrophy (FSHD). As FSHD is a slowly progressive and clinically heterogeneous disease, imaging biomarkers have been proposed to complement clinical outcome measures. Muscle magnetic resonance imaging (MRI), ultrasound and dual energy X-ray absorptiometry (DEXA) have been used to measure disease severity, activity and progression. We conducted a scoping review of the literature on these imaging modalities to assess gaps in knowledge and subsequently collaborated with a panel of neuromuscular imaging experts to generate recommendations on the road ahead. We systematically searched PubMed, EMBASE and Cochrane Library databases. Three-hundred and twenty-eight studies were screened and one hundred and five studies were included. MRI indices related to intramuscular fat content, STIR positivity and T2water are used as diagnostic as well as prognostic and monitoring biomarkers. Ultrasound echogenicity can be used as a diagnostic and potentially as a prognostic and monitoring biomarker. DEXA lean muscle mass may be used as an additional monitoring biomarker. Each imaging modality has its own benefits but also challenges. Based on our expert opinions, we propose a roadmap to address these challenges, ensuring the optimal use of each modality in multi-center clinical trials in FSHD.

Applications of X-Ray Radiography and X-Ray Computed Microtomography in Gemology

Applications of X-Ray Radiography and X-Ray Computed Microtomography in Gemology

Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification

Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification

Defect modulation of stable and fast inorganic perovskite Rb2AgCl3 scintillators via Li+ doping for X-ray imaging

Defect modulation of stable and fast inorganic perovskite Rb2AgCl3 scintillators via Li+ doping for X-ray imaging

Correction: Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

Correction: Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

Dental X-Ray image enhancement using a novel evolutionary optimization algorithm

Dental X-Ray image enhancement using a novel evolutionary optimization algorithm

Synchronous Regulation of Charge Transfer and Defects in Perovskite Nanocomposite Films for Enhanced Sensitivity and Stability in Monolithically Integrated X-Ray Imaging Arrays.

Perovskites nanocrystals (PNCs) have garnered significant research interest in X-ray detection due to their strong X-ray absorption capability, and unique advantages in large area and thick film deposition that result from the decoupling of perovskite crystallization from film formation. However, traditional long-chain ligands used in PNCs, such as oleic acid and oleyl amine, suffer from poor conductivity and susceptibility to detachment, which limits the performance of X-ray detectors based on them. In this study, a strategy is proposed to partially replace long-chain ligands with short-chain counterparts like phenethylammoniumbromide (PEABr) and CF3PEABr, during the synthesis of CsPbBr3 PNCs. This approach leads to a lower defect density, enhanced carrier transport, and suppressed ion migration simultaneously in the resulting PNCs. These PNCs are then combined with organic bulk heterojunction to construct the nanocomposite X-ray detectors, which exhibit an impressive sensitivity of 10787 µC Gyair⁻¹ cm⁻2 and stable dark current baseline under a large electric field of ≈17000V cm-1. Finally, a flat panel X-ray imaging sensor is prepared by monolithically integrating the nanocomposite film with a thin-film transistor backplane, enabling high-resolution and real-time X-ray imaging. The image quality is further enhanced through a super-resolution reconstruction approach, effectively facilitating a wide range of practical applications in real-world scenarios.

Diagnostic Role of X-ray Imaging in Renal and Ureteric Calculi Keeping Computed Tomography as Gold Standard

Diagnostic Role of X-ray Imaging in Renal and Ureteric Calculi Keeping Computed Tomography as Gold Standard

Rolling the Dice with Light Competition: Introducing a True Random Number Generator Powered by Photo-Induced Polarity Current.

The pursuit of hardware-based security solutions has highlighted the true random number generator (TRNG). Various physical phenomena, from noise generation to quantum physics complexities, have been explored for random number generation. The arc discharge light-induced TRNG (ALTRNG) is introduced, featuring wavelength-dependent photocurrent generation and arc discharge irradiation. A bipolar photo-responsive photodetector (BPPD) differentiates "1" and "0" states, producing highly random bits validated by the National Institute of Standards and Technology (NIST) 15 tests. The BPPD's response to deep-ultraviolet (DUV) and blue light enables distinct photocurrent generation under arc discharge illumination. The ALTRNG generates true random signals, yielding bit streams with unpredictability, uniform distribution, and stability. With a readout circuit achieving 2-kbps, wireless random number transmission is demonstrated, highlighting potential for secure password systems and artificial X-ray image generation.

Web Based Chest X-ray Classification For Pneumonia Detection Using CNN

Abstract: This project introduces a method that uses deep learning to detect pneumonia by using Convolutional Neural Networks (CNNs). The model is trained on chest X-ray images to accurately tell the difference between healthy lungs and those with pneumonia. By using advanced techniques to process images and sophisticated CNN designs, this system provides an affordable and dependable way to identify pneumonia early. The goal is to support healthcare workers, especially in areas where resources are limited, by offering quick and flexible diagnostic help.

Anteroinferior iliac spine osteoplasty at the time of periacetabular osteotomy helps preserve preoperative range of motion

Abstract Iatrogenic femoroacetabular impingement following periacetabular osteotomy (PAO) is a well-documented cause of postoperative complications. The anterior inferior iliac spine (AIIS) is an increasingly recognized source of impingement, and previous studies have documented high rates of abnormal AIIS subtypes in dysplastic hips undergoing PAO. This study evaluates the effects of PAO with concomitant AIIS osteoplasty on a range of motion and function. We performed a retrospective study of 63 hips that underwent PAO with concomitant open AIIS osteoplasty. AIIS was classified using 3D-reconstruction Computed tomography (CT) and femoral version was measured on axial-CT. Pre- and post.-operative lateral center-edge angle, Tönnis-angle, and anterior center-edge angle were calculated using X-ray imaging. Hip internal rotation (IR) at 90° flexion was recorded preoperatively, intraoperatively following PAO (pre- and postosteoplasty), and 6 months postoperatively. Pre- and postoperative Merle-d’Aubigné (MDA) scores were compared using pairwise t-test. IR motion changes were compared using repeated measures analysis of variance. Regression analyses evaluated the impact of femoral version on IR before and after osteoplasty. MDA significantly improved postoperatively. Intraoperative IR prior to AIIS osteoplasty was significantly reduced compared to other timepoints, but addition of the osteoplasty improved IR intraoperatively and 6 months postoperatively. No differences were noted in IR between pre- and postoperative examination. The impact of IR restoration with AIIS osteoplasty correlated significantly with femoral version, with greater motion improvement noted in patients with lower version. When planning PAO, careful appreciation of motion parameters is critical. Regardless of AIIS morphology, consideration of an intraoperative AIIS osteoplasty should occur when IR is decreased following acetabular reorientation.

The Correlation Between Gender, Age, Curvature, and Symptom-related Changes in C6 and C7 Slope in 10,000 Subjects.

A cross-sectional analysis of 10,000 cervical spine X-rays. This study investigates the variations in C6S and C7S across demographic factors (gender, age, cervical curvature, symptoms) and explores their correlation. Additionally, machine learning models are applied to improve the accuracy of C7S prediction. The C7S is crucial for assessing cervical balance but is often limited by visibility issues. This study uses a large sample to validate the feasibility of the C6S as a substitute for C7S across diverse populations with varying ages, genders, symptoms, and cervical curvatures. A retrospective study was conducted on 10,000 subjects who underwent cervical sagittal X-ray imaging. Four orthopedic specialists labeled key points, which were cross-validated, and an algorithm was then used to measure C6S and C7S. Pearson correlation coefficients were calculated to assess the relationship between C6S and C7S, and linear regression derived a predictive equation for C7S. Various machine learning models were compared to improve C7S prediction accuracy. The average angles for C6S and C7S were 15.4° (16.8° in males, 14.7° in females) and 19.1° (21.1° in males, 18.2° in females), respectively, with C7S generally larger than C6S, except in Sigmoid 1 curvature. Males exhibited higher values for both C6S and C7S, and both slopes increased after age 20. Both angles increased significantly with age from 20 to 90 years. A strong positive correlation was found between C6S and C7S (r>0.75, P<0.001), confirmed by linear regression (R²=0.688). Among the machine learning models, both Ridge Regression and Linear Regression performed better than the others, with R²=0.855 in predicting C7S. The strong correlation between C6S and C7S suggests that C6S can substitute for C7S when visibility is limited. Machine learning models further enhance prediction accuracy, demonstrating promising clinical potential.

Automatic Identification of Adenoid Hypertrophy via Ensemble Deep Learning Models Employing X-ray Adenoid Images.

Adenoid hypertrophy, characterized by the abnormal enlargement of adenoid tissue, is a condition that can cause significant breathing and sleep disturbances, particularly in children. Accurate diagnosis of adenoid hypertrophy is critical, yet traditional methods, such as imaging and manual interpretation, are prone to errors. This study uses an ensemble deep learning-based approach for adenoid classification. It utilizes a unique dataset sourced from Batman Training and Research Hospital. The dataset is composed of masked and non-masked X-ray images. It is used to train and compare the performance of multiple convolutional neural network (CNN) models. By comparing classification accuracy between masked and non-masked datasets, the study reveals the importance of image preprocessing. Six deep learning models-EfficientNet, MobileNet, ResNet50, ResNet152, VGG16, and Xception-are tested, with ResNet50 achieving the highest accuracy (100% on masked images), while Xception performs the worst (65% F1-score). The results indicate that masking significantly enhances the accuracy and reliability of adenoid classification. ResNet50 and EfficientNet show strong generalization capabilities. Conversely, the lower performance of models like Xception highlights the variability in model suitability for this task. This research provides valuable insights into optimizing deep learning models for medical image classification and it advances the field of AI-based adenoid detection.

Discovery of polarized X-ray emission from the accreting millisecond pulsar SRGA J144459.2–604207

We report the discovery of polarized X-ray emission from an accreting millisecond pulsar. During a 10-day-long coverage of the February 2024 outburst of SRGA J144459.2−604207, the Imaging X-ray Polarimetry Explorer (IXPE) detected an average polarization degree of the 2–8 keV emission of 2.3%±0.4% at an angle of 59° ±6° (east of north; the uncertainties quoted are at the 1σ confidence level). The polarized signal shows a significant energy dependence with a degree of 4.0%±0.5% between 3 and 6 keV and &lt; 1.5% (90% c.l.) in the 2–3 keV range. We used NICER, XMM–Newton, and NuSTAR observations to obtain an accurate pulse-timing solution and to perform a phase-resolved polarimetric analysis of IXPE data. We did not detect any significant variability in the Stokes parameters Q and U with the spin and orbital phases. We used the relativistic rotating-vector model to show that a moderately fan-beam emission from two point-like spots at low magnetic obliquity (≃10°) is compatible with the observed pulse profile and polarization properties. IXPE also detected 52 type I X-ray bursts whose recurrence time Δtrec increased from 2 to 8 h as a function of the observed count rate C as Δtrec ∝ C−0.8. We stacked the emission observed during all the bursts and obtained an upper limit on the polarization degree of 8.5% (90% c.l.).

Optimizing dental implant identification using deep learning leveraging artificial data

This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create implant surface models. Subsequently, implant surface models were used to generate two-dimensional X-ray images, which were compiled along with original images to create a comprehensive dataset. Images of 10 types of implants were classified using ResNet50 into the following datasets: (A) images of implants captured in vivo, (B) artificial implant images generated without background adjustments, and (C) implant images derived from in vivo images and generated with background adjustments. The classification accuracy was 0.8888 for dataset A, 0.903 for dataset B, and 0.9146 for dataset C. Notably, dataset C demonstrated the highest performance and exhibited the optimal feature distribution. In the context of deep learning classifiers for dental implants using panoramic X-ray images, incorporating artificially generated X-ray images—designed to mirror the appearance of human body implants—proved to be the most beneficial in enhancing the performance of the classification model.

P-736. No Air Don’t Care: Lack of gas on imaging is associated with a longer time to surgical intervention in patients with necrotizing fasciitis

Abstract Background Although necrotizing fasciitis is a life-threatening infection, initial symptoms may be non-specific. The presence of gas on plain X-ray or computed tomography scan can be diagnostic, but is missing in up to 1/4 of cases.Figure 1.Stratification of patients with necrotizing fasciitis Methods We performed a retrospective cohort study of adult patients admitted in 2022-2023 with necrotizing fasciitis (diagnosis code M72.6) at an urban level 1 trauma center, who received surgical intervention, and at least 1 dose of linezolid or clindamycin in addition to other antibiotics. Patients transferred from an outside hospital, who had no imaging results, or no surgical intervention were excluded. Patients with and without gas on initial imaging result were compared (Fig1). Primary outcome was whether a lack of gas on imaging correlated with longer time to surgical intervention. Time 1 was defined as the time radiographic images were attested, or the time of surgery consult (whichever was later). Time 2 was the documented operating room (OR) surgical start time. Clinical and microbiologic outcomes were also recorded.Table 1:Characteristics of patients with necrotizing soft-tissue infection with and without gas on initial imaging Results 79 patients were included in the analysis (60 and 19 patients with and without gas on imaging, respectively). Cohorts were evenly matched in terms of illness severity and comorbidities, except that patients without gas on imaging were more often female (P=0.014) with higher rates of CPK elevation (P=0.033) (Table 1). Site of infection did not differ between groups (Fig2), nor did microbiologic OR culture results (figure not included, P=0.105). Patients without gas on imaging had significantly longer time to OR compared to those with gas (median 15.7 verses 4.3 hours, respectively, P=0.0128) (Figure 3a). Length of stay, repeat trips to OR, and mortality did not differ between groups, however, patients without gas had high rates of readmission (P=0.041) (Fig3b). Conclusion Almost a quarter of necrotizing fasciitis patients presented without gas on initial imaging result. Despite having a similar degree of illness and poor prognostic laboratory parameters, these patients had a significantly longer time to OR compared to those with gas on initial imaging. Infectious diseases physicians should strive to provide valuable insight to our surgical colleagues for a syndromic approach to this illness regardless of the imaging results. Disclosures All Authors: No reported disclosures

Transimpedance Matrix Measurement (TIM) Parameters Evaluation for the Assessment of Cochlear Implant Electrode Placement and Modiolar Proximity in Children

Introduction: Transimpedance matrix measurement (TIM) is an electrophysiological measurement protocol of the impedance patterns of electrode contacts within the cochlea. Several studies have reported that TIM is an effective tool for the identification of abnormal electrode array placement. However, the normative values for properly inserted electrodes, as well as correlation of the TIM patterns with the electrode position, are not completely determined. Objectives: The first aim of this study is to establish normative values of TIM measurements obtained in children with proper electrode array insertion and tip fold-over, with proper inner ear anatomy and in congenital anomalies. The second aim of this study is to compare TIM measurements in Slim Modiolar (SM) and in Contour Advance (CA) electrodes, as their position is different according to the modiolus proximity. Methods: A total of 55 pediatric patients were included in the study and underwent cochlear implantation. 62 intraoperative measurements were conducted in this group—50 in children with normal inner ear anatomy and 12 in children with inner ear malformations. After each implantation, a plain x-ray was obtained. Results: There were clear statistically significant differences in TIM patterns in patients where electrode fold-over was confirmed and between SM and CA electrodes. Conclusions: TIM is a promising technique for intraoperative analysis of electrode placement. TIM patterns differ and correlate consistently with the different models of array implanted. This study is the first to report TIM patterns observed in children with normal inner ear anatomy and in inner ear malformations.

Tuberculosis Detection Using Convolutional Neural Network

Tuberculosis (TB) is a chronic lung disease caused by bacterial infection and remains one of the leading causes of mortality worldwide. Early and accurate detection of TB is crucial for effective treatment and prevention of complications. In this study, we propose a deep learning-based system for TB detection using chest X-ray images. The system leverages the VGG16 architecture for classification, supported by image preprocessing and data augmentation to improve model performance. The dataset comprises X-ray images categorized into TB-infected and normal cases. The VGG16 model was trained, validated, and tested to classify chest X-rays, achieving a train accuracy of 98.55%, validation accuracy of 99.75% and test accuracy of 98.73%, for TB detection. Our results demonstrate the effectiveness of the VGG16-based model in reliably detecting TB from chest X-rays while providing practical healthcare accessibility through the user interface. This combined approach can serve as a valuable tool in computer-aided TB diagnosis, enabling timely and accurate detection to improve patient outcomes. Key Words: Chest X-ray Classification, Deep Learning, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Transfer Learning, TB Diagnosis.