Objective: The average glandular dose (AGD) is used to evaluate the radiation dosage in mammography. Dance et al. (2000) presented a computation formula to estimate the AGD based on several coefficient factors, such as compressed breast thickness, breast tissue composition, and half-value layers (HVLs). The objective of this study was to improve the preciseness of AGD estimation. Materials and Methods: We interpolated the coefficients developed by Dance et al. to generate an approximation formulae and reference datasets with higher granularity and breast thickness (2–6 cm) relevant to a Japanese population. Results: The results from this study indicate that the incorporation of HVLs and breast thickness required in mammography densitometry leads to an advancement in the current method for estimating the average glandular dose. Conclusions: We expect that these interpolated values will serve as a reference for other researchers and allow for a more accurate, detailed, and individualized AGD estimation.

We aimed to investigate the effects of mammary gland density and average glandular dose (AGD) on contrast-to-noise ratio (CNR) of breast-equivalent phantoms with different mammary gland/fat tissue ratios. Full-field digital-mammography breast X-rays were performed on breast-equivalent phantoms with three different mammary gland/fat tissue ratios (Phantom A [30/70], Phantom B [50/50], and Phantom C [70/30]) and seven thicknesses ranging from 10 mm to 70 mm. The prediction formula for the CNR was calculated by multivariate analysis and the effects of the various parameters on CNR were evaluated using a multiple regression analysis model. Higher CNR values were obtained with lower mammary gland/fat tissue ratios and lower phantom thicknesses. Variation in CNR among the three breast models was low (coefficient of variation, 3.4–8.7%) at lower phantom thicknesses (10–30 mm) and high (coefficient of variation, 10.5–16.8%) at higher phantom thickness (50–70 mm). CNR showed a strong negative correlation (r = -0.8989) with AGD across all three mammary gland ratios. A predictive formula for CNR using AGD and mammary gland density was developed. CNR can be predicted with high precision using AGD and mammary gland density. The predicted CNR could be used to measure the diagnostic reliability of mammography in breast cancer.