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Effect of Different Breast Densities and Average Glandular Dose on Contrast to Noise Ratios in Full-Field Digital Mammography: Simulation and Phantom Study

机译:不同乳腺密度和平均腺体剂量对全场数字乳房X线摄影中噪声比对比的影响:仿真与幻影研究

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摘要

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.
机译:我们的目标是探讨乳腺密度和平均腺剂量(AGD)对乳腺当量模糊与不同乳腺/脂肪组织比率的对比噪声比(CNR)的影响。全场数码乳腺X线摄影乳腺X射线是对具有三种不同乳腺/脂肪组织比率的乳腺当量模拟(Phantom A [30/70],Phantom B [50/50]和Phantom C [70/30] ])和七厚度范围为10毫米至70毫米。通过多变量分析计算CNR的预测公式,并使用多元回归分析模型评估各种参数对CNR上的各种参数的影响。通过较低的乳腺/脂肪组织比和更低的体致厚度获得较高的CNR值。三种乳房模型中CNR的变化低(变异系数,3.4-8.7%),低体​​模厚度(10-30mm),高(变异系数,10.5-16.8%),在更高的体模厚度(50-70毫米)。 CNR在所有三个乳腺比率上显示出强烈的负相关(R = -0.8989),患有AGD。开发了使用AGD和乳腺密度的CNR预测公式。可以使用AGD和乳腺密度来预测CNR的预测性高精度。预测的CNR可用于测量乳腺癌乳腺X线摄影的诊断可靠性。

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