Tu and Yuen Center for Functional Onco-Imaging, University of California, Irvine, California 92697, USA.
Med Phys. 2010 Jan;37(1):227-33. doi: 10.1118/1.3271353.
In breast MRI, skin and fibroglandular tissue commonly possess similar signal intensities, and as such, the inclusion of skin as dense tissue leads to an overestimation in the measured density. This study investigated the impact of skin to the quantitative measurement of breast density using MRI.
The analysis was performed on the normal breasts of 50 women using nonfat-saturated (nonfat-sat) T1 weighted MR images. The skin was segmented by using a dynamic searching algorithm, which was based on the change in signal intensities from the background air (dark), to the skin (moderate), and then to the fatty tissue (bright). Tissue with moderate intensities that fell between the two boundaries determined based on the intensity gradients (from air to skin, and from skin to fat) was categorized as skin. The percent breast density measured with and without skin exclusion was compared. Also the relationship between the skin volume and the breast volume was investigated. Then, this relationship was used to estimate the skin volume from the breast volume for skin correction.
The percentage of the skin volume normalized to the breast volume ranged from 5.0% to 15.2% (median 8.6%, mean +/- STD 8.8 +/- 2.6%) among 50 women. The percent breast densities measured with skin (y) and without skin (x) were highly correlated, y = 1.23x+7% (r = 0.94, p < 0.001). The relationship between the skin volume and the breast volume was analyzed based on transformed data (the square root of the skin volume vs the cube root of breast volume) using the linear regression, and yielded r = 0.87, p < 0.001. When this model was used to estimate the skin volume for correction in the density analysis, it provided a better fit to the measured density with skin exclusion (with adjusted R2 = 0.98, and root mean square error = 1.6) compared to the correction done by using a fixed cutoff value of 8% (adjusted R2 = 0.83, root mean square error = 4.7).
The authors have shown that the skin volume is related to the breast volume, and this relationship may be used to correct for the skin effect in the MRI-based density measurement. A reliable quantitative density analysis method will aid in clinical investigation to evaluate the role of breast density for cancer risk assessment or for prediction of the efficacy of risk-modifying drugs using hormonal therapy.
在乳腺 MRI 中，皮肤和纤维腺体组织通常具有相似的信号强度，因此，将皮肤包含为致密组织会导致测量密度的高估。本研究探讨了皮肤对 MRI 测量乳腺密度的定量测量的影响。
使用非脂肪饱和（非脂肪饱和）T1 加权 MR 图像对 50 名女性的正常乳房进行分析。使用基于信号强度从背景空气（暗）到皮肤（中等）再到脂肪组织（亮）变化的动态搜索算法对皮肤进行分割。在基于强度梯度（从空气到皮肤，从皮肤到脂肪）确定的两个边界之间的中等强度的组织被归类为皮肤。比较有皮肤和无皮肤排除时测量的乳腺密度百分比。还研究了皮肤体积与乳房体积之间的关系。然后，使用此关系从乳房体积估计皮肤体积以进行皮肤校正。
50 名女性中，皮肤体积与乳房体积之比的百分比范围为 5.0%至 15.2%（中位数 8.6%，平均值 +/- 标准差 8.8 +/- 2.6%）。有皮肤（y）和无皮肤（x）时测量的乳腺密度百分比高度相关，y = 1.23x+7%（r = 0.94，p <0.001）。使用线性回归分析基于变换数据（皮肤体积的平方根与乳房体积的立方根）分析皮肤体积与乳房体积之间的关系，并得到 r = 0.87，p <0.001。当将此模型用于校正密度分析中的皮肤体积时，与使用固定截止值 8%（调整后的 R2 = 0.83，均方根误差 = 4.7）相比，它为有皮肤排除的测量密度提供了更好的拟合（调整后的 R2 = 0.98，均方根误差 = 1.6）。
作者表明，皮肤体积与乳房体积有关，这种关系可用于校正 MRI 基于密度测量的皮肤效应。可靠的定量密度分析方法将有助于临床研究，以评估乳腺密度在癌症风险评估或激素治疗预测风险改变药物疗效中的作用。
