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System Optimization For Breast Sonography

乳腺超声检查系统优化

基本信息

批准号：
7741676
负责人：
Michael F Insana
金额：
$ 25.47万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-03-01 至 2011-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7741676
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 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 operation performance tests processing speed programs response spatiotemporal theories

项目摘要

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）：本项目旨在开发一种第一原理方法，用于设计和评价乳腺超声检查，特别是用于癌症检测和恶性病变鉴别。该方法是基于贝叶斯理想观察者模型的乳腺超声成像，揭示了最佳的战略，最大限度地提高诊断信息内容的基础上形成图像的具体检查。我们目前的近似理想的观察者策略是使用编码脉冲激励方法与一系列的空间维纳滤波器应用于数据从一个固定的焦点，大孔径阵列。在这个项目中提出的扩展包括迭代维纳滤波器扩展的方法，高度非平稳散射介质，和开发的理想观察员预波束形成的回波数据，允许优化的波束形成策略，为特定的临床任务。初步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，病变可见性可以比标准波束成形显著提高。我们希望从图像形成的基本理论出发，通过在临床仪器上实施最有前途的方法，并最终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带来新的视角。我们正在开发一种新的方法，超声乳腺癌成像，显示了巨大的前景，为显着改善诊断。如果成功，超声检查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。