Improvement of microcalcification detection in digital breast tomosynthesis

描述（由申请人提供）：乳房x光筛查具有有限的敏感性和特异性。数字乳腺断层合成（DBT）是一种新兴的模式，已被证明可以显著提高软组织病变的检测和表征。然而，最初的研究表明，微小的微钙化（MC）团簇通常是早期乳腺癌的唯一征兆，但在DBT中很难观察到。一些人建议DBT与FFDM在筛选中并行使用（即，在两视图FFDM中添加一视图或两视图DBT，以便FFDM可用于MC检测，而DBT可用于质量检测）。这种方法会增加成像成本、阅读时间和患者剂量，这些都是将DBT引入临床实践的主要问题。密歇根大学计算机辅助诊断研究实验室（UM）和GE全球研究中心（GE）之间拟议的合作伙伴关系的主要目标是开发一种综合实用的方法，在不增加患者剂量的情况下解决DBT中MC可视化和检测问题，从而促进DBT最终替代FFDM。为了实现这一目标，我们提出了两个具体目标：（SA1）开发专门设计的MC增强方法，以提高DBT中MC的人和机器可视化，并开发计算机辅助检测（CAD）系统来突出重要的MC集群；（SA2）在DBT工作站中实现开发的MC增强和CAD读取工具，并进行观察者性能研究，比较DBT和FFDM中的MC检测。为实现具体目标，将开展以下工作：(1)进行幻影研究，以确定数据收集的最佳图像采集参数集，(2)收集人类受试者DBT数据库，用于开发算法和观察者研究，(3)开发病变特异性重建和MC增强方法，以提高DBT中MCs的可见性，供放射科医生阅读和计算机检测，(4)开发计算机视觉方法来检测MC候选体，(5)发展MC分析方法以减少假阳性（FPs）和不显著的CAD标记；(6)设计双视图分析以进一步降低FPs；(7)研究MC检测对重建方法和断层合成采集参数的依赖；(8)设计一个DBT工作站，实现MC增强和CAD辅助工具，以突出重要的MC，供放射科医生阅读。我们假设，专门设计的DBT显示系统可以帮助放射科医生在DBT中检测MCs，其准确性至少与FFDM相当。为了验证这一假设，我们将(9)进行观察者ROC研究，以比较三种情况下MCs的检测准确性：(a)不带CAD的双视图DBT与不带CAD的双视图FFDM， (b)带CAD的双视图DBT与带CAD的双视图FFDM，以及(c) cc视图FFDM +带CAD的mlo视图DBT与带CAD的双视图FFDM的特殊协议。

DESCRIPTION (provided by applicant): Screening mammography has limited sensitivity and specificity. Digital Breast Tomosynthesis (DBT) is an emerging modality that has been shown to significantly improve the detection and characterization of soft- tissue lesions. However, initial studies have shown that subtle microcalcification (MC) clusters, which are often the only sign of early breast cancer, can be difficult to visualize in DBT. Some have suggested that DBT be used in parallel with FFDM in screening, (i.e., adding one- or two-view DBT to the two-view FFDMs so that FFDM could be used for MC detection while DBT could be used for mass detection). This approach would increase imaging costs, reading time, and patient dose, which are all major concerns with regards to introducing DBT into clinical practice. The main goal of the proposed Partnership between the University of Michigan Computer-Aided Diagnosis Research Laboratory (UM) and GE Global Research (GE) is to develop an integrated practical approach to resolving the MC visualization and detection problems in DBT without increasing patient dose, thereby facilitating the eventual replacement of FFDM by DBT. To achieve this goal, we propose two Specific Aims: (SA1) to develop specially designed MC enhancing methods to improve human and machine visualization of MCs in DBT and develop a computer-aided detection (CAD) system to highlight significant MC clusters, and (SA2) to implement the developed MC-enhancing and CAD reading tools in a DBT workstation and conduct observer performance studies to compare MC detection in DBT with that in FFDM. The following tasks will be conducted to accomplish the specific aims: (1) perform phantom studies to determine the best set of image acquisition parameters for data collection, (2) collect a database of human subject DBTs for development of algorithms and observer study, (3) develop lesion-specific reconstruction and MC enhancing methods to improve the visibility of MCs in DBT for radiologist's reading and computerized detection, (4) develop computer-vision methods to detect MC candidates, (5) develop MC analysis method to reduce false positives (FPs) and insignificant CAD marks, (6) design two-view analysis to further reduce FPs, (7) study dependence of MC detection on reconstruction methods and tomosynthesis acquisition parameters, and (8) design a DBT workstation implemented with the MC-enhancing and CAD- assisted tools to highlight significant MCs for radiologist's reading. We hypothesize that the specially designed DBT display system can assist radiologists in detection of MCs in DBT with accuracy at least comparable to that in FFDM. To test this hypothesis, we will (9) conduct observer ROC studies to compare the detection accuracy of MCs under three conditions: (a) two-view DBT without CAD vs. two-view FFDM without CAD, (b) two-view DBT with CAD vs. two-view FFDM with CAD, and (c) a special protocol of CC-view FFDM plus MLO-view DBT with CAD vs. two-view FFDM with CAD.