Smith Bryce J, Dey Joyoni, Medlock Lacey, Solis David, Kirby Krystal
Physics Department, Mary Bird Perkins Cancer Center, Baton Rouge, LA, USA.
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, USA.
Phys Eng Sci Med. 2025 May 6. doi: 10.1007/s13246-025-01540-2.
Breast tissue is mainly a mixture of adipose and fibro-glandular tissue. Cancer risk and risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists must report the total volume glandular fraction or a BI-RADS classification in screening and diagnostic mammography. In this work, a Maximum Likelihood algorithm accounting for count statistics and scatter is shown to estimate the pixel-wise glandular fraction from mammographic images. The pixel-wise glandular fraction provides information that helps localize dense tissue. The total volume glandular fraction can be calculated from pixel-wise glandular fraction. The algorithm was implemented for images acquired with an anti-scatter grid, and those without using the anti-scatter grid but followed by software scatter removal. The work also studied if presenting the pixel-wise glandular fraction image alongside the usual mammographic image has the potential to improve the contrast-to-noise ratio on micro-calcifications in the breast. The algorithms are implemented and evaluated with TOPAS Geant4 generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of glandular fraction estimation on microcalcification detection. The algorithm was then applied to clinical images with and without microcalcifications. For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 6.6% for the with anti-scatter-grid cases and 7.6% for the software scatter removal (no anti-scatter grid) cases for a range of 2-9 cm compressed breast thickness. Average absolute errors were 4.5% and 4.7% for a range of 2-9 cm compressed breast thickness respectively for the anti-scatter grid and software scatter-removal methods. For higher thickness and glandular fraction, the errors were higher. For the extreme case of 9 cm thickness, the glandular fraction estimation yielded 5%, 13% and 16% mean absolute errors for 20%, 30% and 50% glandular fraction. These errors lowered to 1.5%, 9% and 13.2% for a narrower spectrum for the 9 cm. Results from clinical images (where the true glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in clinical images and 5.1-88% in TOPAS images. A method for accurately estimating the pixel-wise glandular fraction in images, which provides localization information about breast density, was demonstrated. The glandular fraction images also showed an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.
乳腺组织主要是脂肪组织和纤维腺组织的混合物。乳腺癌风险以及未被检测出的乳腺癌风险会随着乳腺中腺组织数量的增加而上升。因此，放射科医生在乳腺筛查和诊断性乳房X光检查中必须报告总体积腺组织分数或BI-RADS分类。在这项研究中，一种考虑计数统计和散射的最大似然算法被证明可从乳房X光图像中估计逐像素的腺组织分数。逐像素的腺组织分数提供了有助于定位致密组织的信息。总体积腺组织分数可根据逐像素的腺组织分数计算得出。该算法针对使用了反散射栅格采集的图像以及未使用反散射栅格但随后进行了软件去散射处理的图像进行了实现。这项研究还探讨了将逐像素的腺组织分数图像与常规乳房X光图像一起呈现是否有可能提高乳腺微钙化的对比度噪声比。这些算法通过TOPAS Geant4生成的具有已知腺组织分数的图像进行了实现和评估。这些图像也在有微钙化和无微钙化的情况下拍摄，以研究腺组织分数估计对微钙化检测的影响。然后该算法被应用于有微钙化和无微钙化的临床图像。对于TOPAS模拟图像，在2至9厘米的压缩乳腺厚度范围内，使用反散射栅格的情况下腺组织分数估计的均方根误差为6.6%，软件去散射（无反散射栅格）的情况下为7.6%。对于2至9厘米的压缩乳腺厚度范围，反散射栅格和软件去散射方法的平均绝对误差分别为4.5%和4.7%。对于更高的厚度和腺组织分数，误差更高。对于9厘米厚度的极端情况，在腺组织分数为20%、30%和50%时，腺组织分数估计的平均绝对误差分别为5%、13%和16%。对于9厘米的较窄光谱范围，这些误差降至1.5%、9%和13.2%。临床图像（真实腺组织分数未知）的结果表明，该算法得出的腺组织分数在文献预期的平均范围内。对于微钙化检测，临床图像中的对比度噪声比提高了17.5 - 548%，TOPAS图像中提高了5.1 - 88%。展示了一种准确估计图像中逐像素腺组织分数的方法，该方法提供了关于乳腺密度的定位信息。腺组织分数图像在检测微钙化（乳腺癌的一个风险因素）的对比度噪声比方面也有改善。
