García Eloy, Diaz Oliver, Martí Robert, Diez Yago, Gubern-Mérida Albert, Sentís Melcior, Martí Joan, Oliver Arnau
Computer Vision and Robotics Institute, University of Girona, Spain.
Tokuyama Laboratory GSIS, Tohoku University, Sendai, Japan.
Eur J Radiol. 2017 Aug;93:121-127. doi: 10.1016/j.ejrad.2017.05.033. Epub 2017 May 30.
The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast.
We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures.
Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution.
This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.
本文旨在使用由重复的乳房X线照片对组成的数据集，评估Volpara™提供的空间腺体体积组织分布以及密度测量值，其中每对照片在短时间内获取，且乳房位置略有变化。
我们对99名不同患者重复获取的99对全视野数字化乳房X线照片进行了回顾性分析。使用商业软件Volpara™密度图（Volpara Solutions，惠灵顿，新西兰）来估计每个图像中的全局和局部腺体组织分布。比较两次采集之间Volpara™提供的全局测量值，如乳房体积、腺体组织体积和体积乳房密度。使用直方图相似性指标（如交集和相关性）以及局部测量值（如差异图像的统计数据和局部梯度相关性测量值）对局部腺体信息进行评估。
无论阳极/滤过材料如何，全局测量值均显示出高度相关性（乳房体积R = 0.99，腺体组织体积R = 0.94，体积乳房密度R = 0.96）。同样，直方图交集和相关性指标表明，对于每对图像，它们共享高度的信息。关于腺体组织的局部分布，视角的微小变化不会在腺体模式上产生显著差异，而两次采集之间乳房厚度的变化会影响实质的空间分布。
本研究表明，Volpara™密度图在估计局部腺体组织分布方面是可靠的，可用于其评估和随访。Volpara™密度图对采集角度和束能量的微小变化具有鲁棒性，尽管由于不同的乳房压迫条件会出现差异。
