Office of Science and Engineering Laboratories, CDRH, U.S. Food and Drug Administration, Silver Spring, MD, 20993-0002, USA.
Med Phys. 2017 Jun;44(6):2304-2311. doi: 10.1002/mp.12230. Epub 2017 Apr 20.
A dual-energy material decomposition method using photon-counting spectral mammography was investigated as a non-invasive diagnostic approach to differentiate between Type I calcifications, consisting of calcium oxalate dihydrate or weddellite compounds that are more often associated with benign lesions, and Type II calcifications containing hydroxyapatite that are predominantly associated with malignant tumors.
The study was carried out by numerical simulation to assess the feasibility of the proposed approach. A pencil-beam geometry was modeled, and the total number of x-rays transported through a breast embedded with microcalcifications of different types and sizes were simulated by a one-pixel detector. Material decomposition using two energy bins was then applied to characterize the simulated calcifications into hydroxyapatite and weddellite using maximum-likelihood estimation, taking into account the polychromatic source, and the energy dependent attenuation. Simulation tests were carried out for different dose levels, energy windows and calcification sizes for multiple noise realizations.
The results were analyzed using receiver operating characteristic (ROC) analysis. Classification between Type I and Type II calcifications achieved by analyzing a single microcalcification showed moderate accuracy. However, simultaneously analyzing several calcifications within the cluster provided area under the ROC curve of greater than 99% for radiation dose greater than 4.8 mGy mean glandular dose.
Simulation results indicated that photon-counting spectral mammography with dual energy material decomposition has the potential to be used as a non-invasive method for discrimination between Type I and Type II microcalcifications that can potentially improve early breast cancer diagnosis and reduce the number of negative breast biopsies. Additional studies using breast specimens and clinical data should be performed to further explore the feasibility of this approach.
本研究旨在探讨一种基于光子计数能谱乳腺摄影的双能物质分解方法,作为一种非侵入性诊断方法,以区分由草酸钙二水合物或鸟粪石化合物组成的更常与良性病变相关的Ⅰ型钙化与主要与恶性肿瘤相关的含羟磷灰石的Ⅱ型钙化。
本研究通过数值模拟来评估所提出方法的可行性。采用铅笔束几何模型,通过一个像素探测器模拟穿过嵌入不同类型和大小微钙化的乳房的总 X 射线数量。然后应用双能能窗,使用最大似然估计法将模拟钙化分解为羟磷灰石和鸟粪石,同时考虑多色源和能量依赖衰减。针对不同的剂量水平、能量窗和钙化大小,针对多个噪声实现进行了模拟测试。
使用受试者工作特征(ROC)分析对结果进行了分析。对单个微钙化进行分析,结果表明分类Ⅰ型和Ⅱ型钙化具有中等准确性。然而,同时分析簇内的多个钙化,则在辐射剂量大于 4.8 mGy 平均腺体剂量时,ROC 曲线下面积大于 99%。
模拟结果表明,具有双能物质分解功能的光子计数能谱乳腺摄影有可能作为一种非侵入性方法,用于区分Ⅰ型和Ⅱ型微钙化,从而可能提高早期乳腺癌的诊断准确性,并减少阴性乳腺活检的数量。应使用乳腺标本和临床数据进行进一步研究,以进一步探索该方法的可行性。