A method to produce and validate a digitally reconstructed radiograph-based computer simulation for optimisation of chest radiographs acquired with a computed radiography imaging system.

OBJECTIVES: The purpose of this study was to develop and validate a computer model to produce realistic simulated computed radiography (CR) chest images using CT data sets of real patients.
METHODS: Anatomical noise, which is the limiting factor in determining pathology in chest radiography, is realistically simulated by the CT data, and frequency-dependent noise has been added post-digitally reconstructed radiograph (DRR) generation to simulate exposure reduction. Realistic scatter and scatter fractions were measured in images of a chest phantom acquired on the CR system simulated by the computer model and added post-DRR calculation.
RESULTS: The model has been validated with a phantom and patients and shown to provide predictions of signal-to-noise ratios (SNRs), tissue-to-rib ratios (TRRs: a measure of soft tissue pixel value to that of rib) and pixel value histograms that lie within the range of values measured with patients and the phantom. The maximum difference in measured SNR to that calculated was 10%. TRR values differed by a maximum of 1.3%.
CONCLUSION: Experienced image evaluators have responded positively to the DRR images, are satisfied they contain adequate anatomical features and have deemed them clinically acceptable. Therefore, the computer model can be used by image evaluators to grade chest images presented at different tube potentials and doses in order to optimise image quality and patient dose for clinical CR chest radiographs without the need for repeat patient exposures.