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Competing Interests: Competing interests: None declared.

Objectives: To develop x-ray simulation software tools to support delivery of radiological science education for a range of learning environments and audiences including individual study, lectures, and tutorials., Methods: Two software tools were developed; one simulated x-ray production for a simple two dimensional radiographic system geometry comprising an x-ray source, beam filter, test object and detector. The other simulated the acquisition and display of two dimensional radiographic images of complex three dimensional objects using a ray casting algorithm through three dimensional mesh objects. Both tools were intended to be simple to use, produce results accurate enough to be useful for educational purposes, and have an acceptable simulation time on modest computer hardware. The radiographic factors and acquisition geometry could be altered in both tools via their graphical user interfaces. A comparison of radiographic contrast measurements of the simulators to a real system was performed., Results: The contrast output of the simulators had excellent agreement with measured results. The software simulators were deployed to 120 computers on campus., Conclusions: The software tools developed are easy-to-use, clearly demonstrate important x-ray physics and imaging principles, are accessible within a standard University setting and could be used to enhance the teaching of x-ray physics to undergraduate students., Advances in Knowledge: 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., (Copyright © 2017. Published by Elsevier Ltd.)

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 1 to L 4 ) were obtained. The European Spine Phantom was used to cross-calibrate the 7 centers and 11 scanners. Reference curves were produced for L 1 to L 4 bone mineral apparent density (BMAD) and total body less head (TBLH) and L 1 to L 4 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., (© 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.