System Optimization For Breast Sonography

乳腺超声检查系统优化

• 批准号: 7557863
• 负责人: Michael F Insana
• 金额: $ 25.73万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7557863
• 关键词: Acoustics; Algorithms; Area; Benign; Breast; Breast Cancer Detection; Cancer Detection; Characteristics; Clinical; Clinical Research; Clinical Trials; Code; Data; Data Display; Detection; Development; Diagnosis; Diagnostic; Discrimination; Electronics; Engineering; Ensure; Environment; Equation; Equilibrium; Equipment; Evaluation; Future; Human; Image; Imaging Techniques; Institution; Investigation; Lead; Lesion; Light; Literature; Malignant - descriptor; Mammography; Measurement; Measures; Mechanics; Methods; Modality; Modeling; Noise; Operative Surgical Procedures; Output; Patients; Performance; Physiologic pulse; Play; Predictive Value; Probability; Process; Psychophysiology; ROC Curve; Research; Research Personnel; Resolution; Role; Running; Safety; Scanning; Screening for cancer; Screening procedure; Sensitivity and Specificity; Series; Signal Transduction; Specific qualifier value; Sum; Surface; System; Task Performances; Techniques; Temperature; Testing; Texture; Time; Tissues; Training; Translating; Translations; Ultrasonics; Ultrasonography; Vision research; Visual; Visual system structure; analytical method; base; breast lesion; cancer imaging; computerized data processing; cost; design; echo detection; flexibility; ideal observer (Bayesian); improved; indexing; insight; instrument; malignant breast neoplasm; mathematical model; novel strategies; performance tests; processing speed; programs; response; spatiotemporal; theories

DESCRIPTION (provided by applicant): This project aims to develop a first-principles approach to the design and evaluation of breast sonography specifically for cancer detection and malignant lesion discrimination. The approach is based on the Bayesian ideal observer model for breast sonography that reveals optimal strategies for image formation based on maximizing diagnostic information content for specific examinations. Our current approximation to the ideal observer strategy is to use coded-pulse excitation methods with a series of spatial Wiener filters applied to data from a fixed-focus, large-aperture array. Extensions proposed in this project include iterative Wiener filters to extend the method to highly nonstationary scattering media, and development of the ideal observer for pre-beamformed echo data that allows optimization of beamforming strategies for specific clinical tasks. Preliminary imaging results and studies involving psychophysical testing have shown that lesion visibility can be significantly improved over standard beamforming via dynamic receive focusing. We wish to develop this new imaging approach from the basic theory of image formation, through implementation of the most promising methods on a clinical instrument, and finally testing with tissue mimicking phantoms. These investigations may lead to new optimal techniques for breast imaging and a new perspective on how to design and evaluate all ultrasonic imaging techniques. We are developing a novel approach to ultrasonic breast cancer imaging that shows great promising for significant improvements in diagnosis. If successful, sonography will play a more prominent role in cancer screening using techniques that are highly effective, safe and low cost.

描述(由申请人提供)：该项目旨在开发一种第一原则的方法来设计和评估专门用于癌症检测和恶性病变识别的乳房超声检查。该方法基于乳腺超声的贝叶斯理想观察者模型，该模型基于最大化特定检查的诊断信息内容来揭示成像的最佳策略。我们目前近似的理想观察者策略是使用编码脉冲激励方法，并将一系列空间维纳滤波器应用于来自固定焦距的大口径阵列的数据。该项目中提出的扩展包括迭代Wiener滤波器以将该方法扩展到高度非平稳的散射介质，以及开发用于预波束形成的回波数据的理想观测器，该观测器允许针对特定的临床任务优化波束形成策略。初步的成像结果和涉及心理物理测试的研究表明，通过动态接收聚焦，病变可见性可以显著高于标准波束形成。我们希望从成像的基本理论出发，通过在临床仪器上实施最有希望的方法，最后用组织模拟体模进行测试，来开发这种新的成像方法。这些研究可能会为乳房成像带来新的最佳技术，并为如何设计和评估所有超声成像技术提供一个新的视角。我们正在开发一种新的超声乳腺癌成像方法，该方法显示出在诊断方面有很大的改善前景。如果成功，超声波将在癌症筛查中发挥更突出的作用，使用高效、安全和低成本的技术。