Development of a method for automating effective patient diameter estimation for digital radiography

Abstract

National patient dose audit of paediatric radiographic examinations is complicated by a lack of data containing a direct measurement of the patient diameter in the examination orientation or height and weight. This has meant that National Diagnostic Reference Levels (NDRLs) for paediatric radiographic examinations have not been updated in the UK since 2000, despite significant changes in imaging technology over that period.

This work is the first step in the development of a computational model intended to automate an estimate of paediatric patient diameter. Whilst the application is intended for a paediatric population, its development within this thesis uses an adult cohort. The computational model uses the radiographic image, the examination exposure factors and a priori information relating to the x-ray system and the digital detector.

The computational model uses the Beer-Lambert law. A hypothesis was developed that this would work for clinical exposures despite its single energy photon basis. Values of initial air kerma are estimated from the examination exposure factors and measurements made on the x-ray system. Values of kerma at the image receptor are estimated from a measurement of pixel value made at the centre of the radiograph and the measured calibration between pixel value and kerma for the image receptor. Values of effective linear attenuation coefficient are estimated from Monte Carlo simulations. Monte Carlo simulations were created for two x-ray systems. The simulations were optimised and thoroughly validated to ensure that any result obtained is accurate. The validation process compared simulation results with measurements made on the x-ray units themselves, producing values for effective linear attenuation coefficient that were demonstrated to be accurate.

Estimates of attenuator thickness can be made using the estimated values for each variable.

The computational model was demonstrated to accurately estimate the thickness of single composition attenuators across a range of thicknesses and exposure factors on three different x-ray systems. The computational model was used in a clinical validation study of 20 adult patients undergoing AP abdominal x-ray examinations. For 19 of these examinations, it estimated the true patient thickness to within ±9%. This work presents a feasible computational model that could be used to automate the estimation of paediatric patient thickness during radiographic examinations allowing for automation of paediatric radiographic dose audit.

Date of Award	2019
Original language	English
Awarding Institution	University of Dundee
Supervisor	David Sutton (Supervisor) & Sarah Vinnicombe (Supervisor)