Halve the dose while maintaining image quality in paediatric cone beam CT

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Authors

Institutes & Expert groups

  • Université Paris-Descartes
  • KUL - Katholieke Universiteit Leuven
  • KUL - Katholieke Universiteit Leuven - Department of Imaging and Pathology

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DOI

Abstract

Cone beam CT (CBCT) for dentomaxillofacial paediatric assessment has been widely used despite the uncertainties of the risks of the low-dose radiation exposures. The aim of this work was to investigate the clinical performance of different CBCT acquisition protocols towards the optimization of paediatric exposures. Custom-made anthropomorphic phantoms were scanned using a CBCT unit in six protocols. CT slices were blinded, randomized and presented to three observers, who scored the image quality using a 4-point scale along with their level of confidence. Sharpness level was also measured using a test object containing an air/PMMA e,dge. The effective dose was calculated by means of a customized Monte Carlo (MC) framework using previously validated paediatric voxels models. The results have shown that the protocols set with smaller voxel size (180 µm), even when decreasing exposure parameters (kVp and mAs), showed high image quality scores and increased sharpness. The MC analysis showed a gradual decrease in effective dose when exposures parameters were reduced, with an emphasis on an average reduction of 45% for the protocol that combined 70 kVp, 16 mAs and 180 µm voxel size. In contrast, both "ultra-low dose" protocols that combined a larger voxel size (400 µm) with lower mAs (7.4 mAs) demonstrated the lowest scores with high levels of confidence unsuitable for an anatomical approach. In conclusion, a significant decrease in the effective dose can be achieved while maintaining the image quality required for paediatric CBCT

Details

Original languageEnglish
Article number5521
Pages (from-to)1-9
Number of pages9
JournalScientific Reports
Volume9
DOIs
StatePublished - 2 Apr 2019

Keywords

  • Cone beam CT, Dental CBCT, radiation exposure, dentistry, Monte Carlo calculations

ID: 5034931