The increasing use of dual-energy X-ray absorptiometry (DXA) in children has led to the need for robust reference data for interpretation of scans in daily clinical practice. Such data need to be representative of the population being studied and be "future-proofed" to software and hardware upgrades. The aim was to combine all available pediatric DXA reference data from seven UK centers to create reference curves adjusted for age, sex, ethnicity, and body size to enable clinical application, using in vivo cross-calibration and making data back and forward compatible. Seven UK sites collected data on GE Lunar or Hologic Scanners between 1996 and 2012. Males and females aged 4 to 20 years were recruited (n = 3598). The split by ethnic group was white 2887; South Asian 385; black Afro-Caribbean 286; and mixed heritage 40. Scans of the total body and lumbar spine (L to L ) were obtained. The European Spine Phantom was used to cross-calibrate the 7 centers and 11 scanners. Reference curves were produced for L to L bone mineral apparent density (BMAD) and total body less head (TBLH) and L to L areal bone mineral density (aBMD) for GE Lunar Prodigy and iDXA (sex- and ethnic-specific) and for Hologic (sex-specific). Regression equations for TBLH BMC were produced using stepwise linear regression. Scans of 100 children were randomly selected to test backward and forward compatibility of software versions, up to version 15.0 for GE Lunar and Apex 4.1 for Hologic. For the first time, sex- and ethnic-specific reference curves for lumbar spine BMAD, aBMD, and TBLH aBMD are provided for both GE Lunar and Hologic scanners. These curves will facilitate interpretation of DXA data in children using methods recommended in ISCD guidelines. The databases have been created to allow future updates and analysis when more definitive evidence for the best method of fracture prediction in children is agreed. © 2016 American Society for Bone and Mineral Research.

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.

Obesity is a global epidemic and there remains uncertainty over the effect of obesity on skeletal health, particularly in the context of osteoporosis. The aim of this study was to 7 investigate associations of body mass index (BMI) and obesity with bone mineral density (BMD) and prevalent vertebral fracture (VF) in men and women aged 62 years. Three hundred and forty two men and women aged 62.5 ± 0.5 years from the Newcastle Thousand Families Study birth cohort, underwent DXA evaluations of femoral neck and lumbar spine BMD, and of the lateral spine for vertebral fracture assessment. The likelihood of prevalent VF was significantly increased in men when compared to women (OR = 2.7, p < 0.001, 95% Cl 1.7-4.4). As BMI increased in women, so did the likelihood of prevalent any-grade VF

Current approaches to teaching x-ray physics in radiological science lack immediacy when linking theory with practice. This method of delivery allows students to engage with the subject in an experiential learning environment.

Dual-energy X-ray absorptiometry (DXA) body composition measurements are widely performed in both clinical and research settings, and enable the rapid and noninvasive estimation of total and regional fat and lean mass tissues. DXA upgrading can occur during longitudinal monitoring or study; therefore, cross calibration of old and new absorptiometers is required. We compared soft tissue estimations from the GE Prodigy (GE Healthcare, Madison, WI) with the more recent iDXA (GE Healthcare) and developed translational equations to enable Prodigy values to be converted to iDXA values. Eighty-three males and females aged 20.1-63.3 yr and with a body mass index range of 17.0-34.4 kg/m were recruited for the study. Fifty-nine participants (41 females and 18 males) comprised the cross-calibration group and 24 (14 females and 10 males) comprised the validation group. Total body Prodigy and iDXA scans were performed on each subject within 24 h. Predictive equations for total and regional soft tissue parameters were derived from linear regression of the data. Measures of lean and fat tissues were highly correlated (R = 0.95-0.99), but significant differences and variability between machines were identified. Bland-Altman analysis revealed significant biases for most measures, particularly for arm, android, and gynoid fat mass (12.3%-22.7%). The derived translational equations reduced biases and differences for most parameters, although limits of agreement exceeded iDXA least significant change. In conclusion, variability in soft tissue estimates between the Prodigy and iDXA were detected, supporting the need for translational equations in longitudinal monitoring. The derived equations are suitable for group analysis but not individual analysis.