ObjectiveTo investigate the in vivo applicability of non-contrast-enhanced hydroxyapatite (HA)-specific bone mineral density (BMD) measurements based on dual-layer CT (DLCT).MethodsA spine phantom containing three artificial vertebral bodies with known HA densities was measured to obtain spectral data using DLCT and quantitative CT (QCT), simulating different patient positions and grades of obesity. BMD was calculated from virtual monoenergetic images at 50 and 200 keV. HA-specific BMD values of 174 vertebrae in 33 patients (66 ± 18 years; 33% women) were determined in non-contrast routine DLCT and compared with corresponding QCT-based BMD values.ResultsExamining the phantom, HA-specific BMD measurements were on a par with QCT measurements. In vivo measurements revealed strong correlations between DLCT and QCT (r = 0.987 [95% confidence interval, 0.963–1.000]; p < 0.001) and substantial agreement in a Bland–Altman plot.ConclusionDLCT-based HA-specific BMD measurements were comparable with QCT measurements in in vivo analyses. This suggests that opportunistic DLCT-based BMD measurements are an alternative to QCT, without requiring phantoms and specific protocols.Key Points • DLCT-based hydroxyapatite-specific BMD measurements show a substantial agreement with QCT-based BMD measurements in vivo. • DLCT-based hydroxyapatite-specific measurements are on a par with QCT in spine phantom measurements. • Opportunistic DLCT-based BMD measurements may be a feasible alternative for QCT, without requiring dedicated examination protocols or a phantom.

Objective This work aims to study (i) the relationship between body mass index (BMI) and knee synovial inflammation using non-contrast-enhanced MRI and (ii) the association of synovial inflammation versus degenerative abnormalities and pain. Materials and methods Subjects with risk for and mild to moderate radiographic osteoarthritis were selected from the Osteoarthritis Initiative. Subjects were grouped into three BMI categories with 87 subjects per group: normal weight (BMI, 20-24.9 kg/m 2), overweight (BMI, 25-29.9 kg/m 2), and obese (BMI, ≥ 30 kg/m 2), frequency matched for age, sex, race, Kellgren-Lawrence grade, and history of knee surgery and injury. Semi-quantitative synovial inflammation imaging biomarkers were obtained including effusion-synovitis, size and intensity of infrapatellar fat pad signal abnormality, and synovial proliferation score. Cartilage composition was measured using T 2 relaxation time and structural abnormalities using the whole-organ magnetic resonance imaging score (WORMS). The Western Ontario and McMasters (WOMAC) Osteoarthritis Index was used for pain assessment. Intra-and inter-reader reproducibility was assessed by kappa values. Results Overweight and obese groups had higher prevalence and severity of all synovial inflammatory markers (p ≤ 0.03). Positive associations were found between synovial inflammation imaging biomarkers and average T 2 values, WORMS maximum scores and total WOMAC pain scores (p < 0.05). Intra-and inter-reader kappa values for imaging biomarkers were high (0.76-1.00 and 0.60-0.94, respectively). Conclusion Being overweight or obese was significantly associated with a greater prevalence and severity of synovial inflammation imaging biomarkers. Substantial reproducibility and high correlation with knee structural, cartilage compositional degeneration, and WOMAC pain scores validate the synovial inflammation biomarkers used in this study.

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Lung cancer is a major cause of death worldwide. As early detection can improve outcome, regular screening is of great interest, especially for certain risk groups. Besides low-dose computed tomography, chest X-ray is a potential option for screening. Convolutional network (CNN) based computer aided diagnosis systems have proven their ability of identifying nodules in radiographies and thus may assist radiologists in clinical practice. Based on segmented pulmonary nodules, we trained a CNN based one-stage detector (RetinaNet) with 257 annotated radiographs and 154 additional radiographs from a public dataset. We compared the performance of the convolutional network with the performance of two radiologists by conducting a reader study with 75 cases. Furthermore, the potential use for screening on patient level and the impact of foreign bodies with respect to false-positive detections was investigated. For nodule location detection, the architecture achieved a performance of 43 true-positives, 26 false-positives and 22 false-negatives. In comparison, performance of the two readers was 42 ± 2 true-positives, 28 ± 0 false-positives and 23 ± 2 false-negatives. For the screening task, we retrieved a ROC AUC value of 0.87 for the reader study test set. We found the trained RetinaNet architecture to be only slightly prone to foreign bodies in terms of misclassifications: out of 59 additional radiographs containing foreign bodies, false-positives in two radiographs were falsely detected due to foreign bodies.

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MEDICAL PHYSICSC hronic obstructive pulmonary disease (COPD) is a major contributor to morbidity and mortality worldwide, with smoking and air pollution being the main risk factors. Its key components are chronic bronchitis and pulmonary emphysema (1). Early diagnosis of this disease is crucial for treatment and smoking cessation programs. However, current widely applied diagnostic tests do not work well for the early stages of this disease: Spirometry shows low sensitivity (2) and strongly depends on patient cooperation (3). Chest radiography lacks sensitivity and is not recommended for COPD diagnosis (4). Chest CT provides three-dimensional information and enables emphysema detection through classification of lung voxels by attenuation (in Hounsfield units) (5,6). An emphysematous voxel of lung tissue is defined as having an attenuation of less than 2950 HU at full inspiration. The resulting fraction of emphysematous lung tissue constitutes the emphysema index (EI). However, attenuation measurements of emphysema are known to vary with patient dose, section thickness, hardware, and reconstruction algorithm (7). Therefore, only an overall EI Background: Dark-field chest radiography allows for assessment of lung alveolar structure by exploiting wave optical properties of x-rays.Purpose: To evaluate the qualitative and quantitative features of dark-field chest radiography in participants with pulmonary emphysema as compared with those in healthy control subjects. Materials and Methods:In this prospective study conducted from October 2018 to October 2020, participants aged at least 18 years who underwent clinically indicated chest CT were screened for participation. Inclusion criteria were an ability to consent to the procedure and stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and any lung condition besides emphysema that was visible on CT images. Participants were examined with a clinical dark-field chest radiography prototype that simultaneously acquired both attenuation-based radiographs and dark-field chest radiographs. Dark-field coefficients were tested for correlation with each participant's CT-based emphysema index using the Spearman correlation test. Dark-field coefficients of adjacent groups in the semiquantitative Fleischner Society emphysema grading system were compared using a Wilcoxon Mann-Whitney U test. The capability of the dark-field coefficient to enable detection of emphysema was evaluated with receiver operating characteristics curve analysis.Results: A total of 83 participants (mean age, 65 years 6 12 [standard deviation]; 52 men) were studied. When compared with images from healthy participants, dark-field chest radiographs in participants with emphysema had a lower and inhomogeneous dark-field signal intensity. The locations of focal signal intensity loss on dark-field images corresponded well with emphysematous areas found on CT images. The dark-field coefficient was negatively correlated with the quantitative CT-based emphysema index (r = 20.54, ...

Background Opioids are frequently prescribed for pain control in knee osteoarthritis patients, despite recommendations by current guidelines. Previous studies have investigated the chondrotoxicity of different opioid subtypes. However, the impact opioids may have on progression of osteoarthritis in vivo remains unknown. The aim of this study was thus to describe the associations between opioid use and knee structural changes and clinical outcomes, over 4 years. Methods Participants with baseline opioid use (n=181) and who continued use for ≥1 year between baseline and 4-year follow-up (n=79) were included from the Osteoarthritis Initiative cohort and frequency matched with non-users (controls) (1:2). Whole-Organ Magnetic Resonance Imaging Scores (WORMS) were obtained, including a total summation score (WORMS total, range 0–96) and subscores for cartilage (0–36), menisci (0–24), and bone marrow abnormalities and subchondral cyst-like lesions (0–18, respectively). Knee Injury Osteoarthritis Outcomes score (KOOS) symptoms, quality of life (QOL), and pain were also obtained at baseline and follow-up (range 0–100; lower scores indicate worse outcomes). Using linear regression models, associations between baseline and longitudinal findings were investigated. As pain may modify observations, a sensitivity analysis was performed for longitudinal findings. All analyses were adjusted for sex, BMI, age, race, and Kellgren-Lawrence grade. Results Opioid users had greater structural degeneration at baseline (WORMS total: Coef. [95% CI], P; 7.1 [5.5, 8.8], <0.001) and a greater increase over 4 years (4.7 [2.9, 6.5], <0.001), compared to controls. Cartilage and meniscus scores increased greater in opioid users, compared to controls (P≤0.001), and findings withstood the adjustment for baseline pain (P≤0.002). All baseline KOOS scores were lower in opioid users compared to controls (P<0.001). QOL loss was greater, when adjusted for baseline KOOS pain (QOL −6.9 [−11.6, −2.1], 0.005). Conclusions Opioid users had worse baseline knee structural degeneration and faster progression. Opioid use was also associated with worse symptoms, pain, and QOL. Furthermore, QOL loss was greater in opioid users compared to controls, when adjusted for baseline KOOS pain, indicating that opioids may not be suited to prevent subjective disease progression in KOA patients.

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems’ and radiologists’ performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75–0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.