17 March 2015 Comparing prediction models for radiographic exposures
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Abstract
During radiographic exposures the milliampere-seconds (mAs), kilovoltage peak (kVp) and source-to-image distance can be adjusted for variations in patient thicknesses. Several exposure adjustment systems have been developed to assist with this selection. This study compares the accuracy of four systems to predict the required mAs for pelvic radiographs taken on a direct digital radiography system (DDR).

Sixty radiographs were obtained by adjusting mAs to compensate for varying combinations of source-to-image distance (SID), kVp and patient thicknesses. The 25% rule, the DuPont™ Bit System and the DigiBit system were compared to determine which of these three most accurately predicted the mAs required for an increase in patient thickness. Similarly, the 15% rule, the DuPont™ Bit System and the DigiBit system were compared for an increase in kVp. The exposure index (EI) was used as an indication of exposure to the DDR. For each exposure combination the mAs was adjusted until an EI of 1500+/-2% was achieved.

The 25% rule was the most accurate at predicting the mAs required for an increase in patient thickness, with 53% of the mAs predictions correct. The DigiBit system was the most accurate at predicting mAs needed for changes in kVp, with 33% of predictions correct.

This study demonstrated that the 25% rule and DigiBit system were the most accurate predictors of mAs required for an increase in patient thickness and kVp respectively. The DigiBit system worked well in both scenarios as it is a single exposure adjustment system that considers a variety of exposure factors.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
W. Ching, W. Ching, J. Robinson, J. Robinson, M. F. McEntee, M. F. McEntee, } "Comparing prediction models for radiographic exposures", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94161J (17 March 2015); doi: 10.1117/12.2081738; https://doi.org/10.1117/12.2081738
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