Common artefacts and pitfalls in equine computed and digital radiography and how to avoid them

A survey of the radiation safety practices of veterinary practitioners during portable equine radiography in Ireland

Veterinary practitioners and other personnel involved in the examination are exposed to ionizing radiation while performing portable radiographs on horses. An online survey was distributed to all Veterinary Council of Ireland-registered practices where the self-reported practice profile is at least 20% equine work. The survey contained questions relating to radiation safety training, protocols, personal dosimetry and lead protection usage, repeat exposures, sedation, and personnel roles during the examination. The aim of the survey was to document the current radiation safety practices of equine veterinary practitioners during portable radiography. The results showed that although adherence to guidance set out by the Environmental Protection Agency (EPA) is reasonably good, compliance rates can be improved. Personal dosemeter usage and repeat rate reduction could particularly benefit from further improvement. This is of the utmost importance in ensuring that occupational radiation exposure to veterinary practitioners is kept to an absolute minimum during their daily practice.

Image processing setting adaptions according to image dose and radiologist preference can improve image quality in computed radiography of the equine distal limb: A cadaveric study

Image processing (IP) in digital radiography has been steadily refined to improve image quality. Adaptable settings enable users to adjust systems to their specific requirements. This prospective, analytical study aimed to investigate the influence of different IP settings and dose reductions on image quality. Included were 20 cadaveric equine limb specimens distal to the metacarpophalangeal and metatarsophalangeal joints. Images were processed with the Dynamic Visualization II system (Fujifilm) using five different IP settings including multiobjective frequency processing, flexible noise control (FNC), and virtual grid processing (VGP). Seven criteria were assessed by three veterinary radiology Diplomates and one veterinary radiology resident in a blinded study using a scoring system. Algorithm comparison was performed using an absolute visual grading analysis. The rating of bone structures was improved by VGP at full dose (P < .05; AUCVGC  = 0.45). Überschwinger artifact perception was enhanced by VGP (P < .001; AUCVGC  = 0.66), whereas image noise perception was suppressed by FNC (P < .001; AUCVGC  = 0.29). The ratings of bone structures were improved by FNC at 50% dose (P < .05; AUCVGC  = 0.44), and 25% dose (P < .001; AUCVGC  = 0.32), and clinically acceptable image quality was maintained at 50% dose (mean rating 2.16; 95.8% ratings sufficient or better). The favored IP setting varied among observers, with higher agreement at lower dose levels. These findings supported using individualized IP settings based on the radiologist's preferences and situational image requirements, rather than using default settings.

An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs.

An Overview of Factors Affecting Exposure Level in Digital Detector Systems and their Relevance in Constructing Exposure Tables in Equine Digital Radiography

The aim of this review is to describe the steps of constructing exposure tables for use of digital detector systems (DRx) in equine practice. Introductory, selected underlying technical aspects of digital radiography are illustrated. Unlike screen-film radiography (SFR), DRx have a uniform signal response of the detector over a large dose range. This enables generation of diagnostic images from exposures that were previously nondiagnostic on SFR, thus reducing retakes. However, with decreasing detector entrance dose, image noise increasingly hampers the image quality. Conversely, unlike the blackening observed on SFR, overexposures can go visibly undetected by the observer. In DRx the numeric exposure indicator value is the only dose-control tool. In digital radiography the challenge is to reduce the dose and reduce the radiation risk to staff whilst maintaining diagnostic image quality. We provide a stepwise method of developing exposure tables as tools for controlling exposure levels. The identified kVp - mAs combinations in the table are derived from the predefined exposure indicator values of the detector system. Further recommendations are given as to how the exposure indicator can be integrated into routine workflow for rechecking the reliability of the formerly identified settings and how these tables might serve a basis for further reduction of the exposure level. Detector quantum efficiency (DQE) is an important parameter of assessing performance of an imaging system. Detectors with higher DQE can generate diagnostic images with a lower dose, thus having a greater potential for dose reduction than detectors with low DQE.