Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Departamento de Imagen, Instituto Nacional de Cancerología, Mexico City, Mexico.
J Appl Clin Med Phys. 2024 May;25(5):e14360. doi: 10.1002/acm2.14360. Epub 2024 Apr 22.
Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms.
The dual-energy decomposition formalism was used to quantify VBD on simulated dual-energy images of anthropomorphic virtual phantoms with known tissue distributions. We simulated 150 phantoms with volumes ranging from 50 to 709 mL and VBD ranging from 15% to 60%. Using these results, we validated a correction for the presence of skin and assessed the method's intrinsic bias and variability. As a proof of concept, the method was applied to 14 sets of clinical dual-energy images, and the resulting breast densities were compared to magnetic resonance imaging (MRI) measurements.
Virtual phantom VBD measurements exhibited a strong correlation (Pearson's ) with nominal values. The proposed skin correction eliminated the variability due to breast size and reduced the bias in VBD to a constant value of -2%. Disagreement between clinical VBD measurements using MRI and dual-energy mammography was under 10%, and the difference in the distributions was statistically non-significant. VBD measurements in both modalities had a moderate correlation (Spearman's = 0.68).
Our results in virtual phantoms indicate that the material decomposition method can produce accurate VBD measurements if the presence of a third material (skin) is considered. The results from our proof of concept showed agreement between MRI and dual-energy mammography VBD. Assessment of VBD using dual-energy images could provide complementary information in dual-energy mammography and tomosynthesis examinations.
乳房密度是乳腺癌的一个重要危险因素,会影响乳房 X 光筛查的灵敏度。基于区域的乳房密度测量方法可能无法准确反映组织分布情况,因此更倾向于使用容积乳房密度(VBD)测量方法。双能乳腺 X 光摄影可以在不做额外的乳房形状假设的情况下进行容积测量。在这项工作中,我们评估了一种双能分解技术在确定 VBD 方面的性能,方法是将其应用于具有已知组织分布的虚拟人体模型的模拟双能图像上。
使用双能分解公式,对具有已知组织分布的虚拟人体模型的模拟双能图像进行 VBD 的量化。我们模拟了 150 个体积在 50 至 709ml 之间、VBD 在 15%至 60%之间的体模。使用这些结果,我们验证了对皮肤存在的修正,并评估了该方法的固有偏差和变异性。作为概念验证,该方法应用于 14 组临床双能图像,将得到的乳房密度与磁共振成像(MRI)测量结果进行比较。
虚拟体模 VBD 测量值与标称值具有很强的相关性(Pearson's )。所提出的皮肤修正消除了因乳房大小引起的可变性,并将 VBD 的偏差降低到一个恒定的-2%值。使用 MRI 和双能乳腺 X 光摄影测量的临床 VBD 之间存在 10%以下的差异,且分布上的差异在统计学上无显著性。两种模态的 VBD 测量值具有中度相关性(Spearman's = 0.68)。
我们在虚拟体模中的结果表明,如果考虑到第三种材料(皮肤)的存在,物质分解方法可以产生准确的 VBD 测量值。概念验证的结果表明,MRI 和双能乳腺 X 光摄影 VBD 之间具有一致性。使用双能图像评估 VBD 可以在双能乳腺 X 光摄影和断层合成检查中提供补充信息。