Image optimization in digital dental radiography

textWhen the United States Air Force completed a multi-million dollar transition to digital dental radiography in 2010 there was no quantitative method available for establishing the appropriate balance of image quality and radiation dose. The objective of this research was to devise a process to fill this need. Through computer simulation and clinical validation the effect of technical parameters on digital dental radiographic image quality was investigated and an optimization method was devised. The Monte Carlo N-Particle Extended (MCNPX) radiation transport code was used to model the DC Planmeca Intra and AC Gendex 770 dental intraoral radiographic units and a unique anthropomorphic phantom simulating dental bitewing anatomy. The Carestream 6100 RVG sensor signal response, noise response, dose rate dependence and reproducibility were determined experimentally, as were their uncertainties and the inter- and intra- radiographic unit variabilities. The experimental measurements were used to calibrate and scale the MCNPX generated data for the optimization analysis. The technical parameters modified in the simulation were peak kilovoltage, (50 through 90) and tube filtration (inherent, 0.1 mm and 0.2 mm added copper). The entrance air KERMA (~720 microGy) at the current clinical technique (63 kVp at 1 milliAmpere-seconds) was used to establish the reference image quality metrics for comparison. Four figures of merit (FOM) were chosen to encompass the impact of variations in the adjustable parameters. With equal weighting of all FOMs and given no limitations on the equipment, the optimal combination of kVp and tube filtration for dental bitewing imaging identified was 90 kVp with 0.1 mm added copper filtration. The optimal technique in the radiographic units' operating range was 70 kVp and 0.1 mm added copper filtration, which could be immediately adopted for a ~50% (+/-17%) entrance dose and ~40% effective dose savings (Planmeca units). In general, the optimization method facilitates image quality standardization across different radiographic units and sensors in a dental clinic. The unique computer model and optimization method used could be easily customized to evaluate any adult or pediatric intraoral imaging task. The results underscore the importance of tailoring the technical parameters to the particular imaging devices in service.Mechanical Engineerin