Improvement of microcalcification detection in digital breast tomosynthesis

数字乳腺断层合成中微钙化检测的改进

• 批准号: 8327742
• 负责人: HEANG-PING CHAN
• 金额: $ 62.87万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-02 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8327742
• 关键词: Address; Affect; Algorithms; Area; Benchmarking; Breast; Breast Cancer Detection; Breast Microcalcification; Clinical; Complement; Computer Vision Systems; Computer-Assisted Diagnosis; Country; Data Analyses; Data Collection; Data Set; Databases; Dependence; Detection; Development; Digital Mammography; Dose; Evaluation; Freezing; Goals; Human; Image; Image Analysis; Imagery; Label; Laboratory Research; Lesion; Mammography; Medical Imaging; Methods; Michigan; Modality; Patients; Performance; Process; Protocols documentation; Public Health; Publications; ROC Curve; Radiology Specialty; Reader; Reading; Reporting; Research; Research Design; Resources; Scanning; Screening procedure; Sensitivity and Specificity; Simulate; Solutions; System; Testing; Time; TimeLine; Tissues; Training; Universities; Woman; base; clinical practice; computer aided detection; computerized; cost; design; digital; digital models; experience; human subject; improved; malignant breast neoplasm; prevent; programs; radiologist; reconstruction; skills; soft tissue; success; tool

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.

描述（由申请人提供）：筛查乳房X线摄影的灵敏度和特异性有限。数字乳房断层合成（DBT）是一种新兴形态，已证明可以显着改善软组织病变的检测和表征。然而，初步研究表明，通常是早期乳腺癌的唯一迹象的微妙微钙化（MC）簇可能很难在DBT中可视化。有些人建议在筛选中与FFDM并行使用DBT（即，在两视频FFDMS中添加一或两视图DBT，以便可以将FFDM用于MC检测，而DBT可以使用DBT进行质量检测）。这种方法将增加成像成本，阅读时间和患者剂量，这都是将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（GE）之间提议的合作伙伴关系的主要目标是开发一种综合的实用方法，以解决DBT中MC可视化和检测问题而不增加患者剂量的情况，从而促进DBT最终替换FFDM。 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.将执行以下任务以完成具体目的：（1）执行幻象研究以确定数据收集数据的最佳图像获取参数集，（2）收集人类受试者DBT的数据库，用于开发算法和观察者研究，（3）开发病变特定的重建和MC提高MC的启用MC的视野（DBT），以提高MC的启动（DBT）。 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放射科医生阅读的重要MC。我们假设特殊设计的DBT显示系统可以帮助放射科医生在DBT中检测到MC，至少与FFDM中的MC相当。为了检验这一假设，我们（9）将（9）进行观察者ROC研究，以比较三个条件下MC的检测准确性：（a）不带CAD的两种视图DBT与没有CAD的两种观察FFDM，（（b）与CAD vss vss cad ffdm fiew fiew ffdm fiew fiew fffdm cad dbt cad db fff cad db cad db fff fff fff fff fff。与带有CAD的两视频FFDM。