Optimizing Projectional Radiographic Imaging of the Abdomen of Obese Patients: An e-Delphi Study

Effects of body part thickness on low-contrast detail detection and radiation dose during adult chest radiography

INTRODUCTION

Differences in patient size often provide challenges for radiographers, particularly when choosing the optimum acquisition parameters to obtain radiographs with acceptable image quality (IQ) for diagnosis. This study aimed to assess the effect of body part thickness on IQ in terms of low-contrast detail (LCD) detection and radiation dose when undertaking adult chest radiography (CXR).

METHODS

This investigation made use of a contrast detail (CD) phantom. Polymethyl methacrylate (PMMA) was utilised to approximate varied body part thicknesses (9, 11, 15 and 17 cm) simulating underweight, standard, overweight and obese patients, respectively. Different tube potentials were tested against a fixed 180 cm source to image distance (SID) and automatic exposure control (AEC). IQ was analysed using bespoke software thus providing an image quality figure inverse (IQFinv ) value which represents LCD detectability. Dose area product (DAP) was utilised to represent the radiation dose.

RESULTS

IQFinv values decreased statistically (P = 0.0001) with increasing phantom size across all tube potentials studied. The highest IQFinv values were obtained at 80 kVp for all phantom thicknesses (2.29, 2.02, 1.8 and 1.65, respectively). Radiation dose increased statistically (P = 0.0001) again with increasing phantom thicknesses.

CONCLUSION

Our findings demonstrate that lower tube potentials provide the highest IQFinv scores for various body part thicknesses. This is not consistent with professional practice because radiographers frequently raise the tube potential with increased part thickness. Higher tube potentials did result in radiation dose reductions. Establishing a balance between dose and IQ, which must be acceptable for diagnosis, can prevent the patient from receiving unnecessary additional radiation dose.

Understanding radiographic decision-making when imaging obese patients: A Think-Aloud study

Introduction

The incidence of obesity has been steadily rising over the last few decades and is having a significant impact upon the health system. In radiography, a particular challenge of imaging obese patients is implementing the as low as reasonably achievable (ALARA) principle when determining radiation dose, and technical and patient‐care adaptations. This study aimed to better understand the decision‐making strategies of experienced radiographers in determining imaging and exposure factor selection in the context of imaging obese patients.

Methods

This study employed a ‘think‐aloud,’ methodology, and eight experienced diagnostic radiographers working in clinical education were recruited to perform routine AP abdominal X‐ray projections on an anthropomorphic phantom. They were asked to simultaneously verbalise emerging thoughts as they considered positioning, exposure selection and image evaluation. This process was repeated with three different phantom sizes, each representing an increased BMI from ‘healthy,’ to, ‘morbidly obese.’ Audio recordings were transcribed and interpreted via Bowman’s (1997) theory of radiographic judgement and decision‐making.

Results

Analysis of interview transcripts identified 12 key concepts considered by experienced radiographers. Differences in radiographic concepts were considered when imaging phantoms of different sizes was demonstrated. A shift from segmental (e.g. positioning) to more environmental factors (e.g. patient comfort) and an increase in the number of verbal considerations with increasing phantom size were identified. The shift in focus of decision‐making stages identified the greater need to consider contextual factors such as patient comfort and repeatability when imaging obese patients.

Conclusion

Experienced radiographers find imaging obese patients challenging and alter their perception of image quality to accommodate for patient presentation. The findings will help inform future research, practice guidelines and learning resources to provide optimal imaging and care for obese patients, especially for student education.