System Optimization For Breast Sonography

乳腺超声检查系统优化

基本信息

批准号：
7257992
负责人：
Michael F Insana
金额：
$ 26.91万
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 2010-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7257992
关键词：
Acoustics Algorithms Area Benign Breast Breast Cancer Detection Cancer Detection Characteristics Clinical Clinical Research Clinical Trials Code Condition Data Data Display Depth 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 Pliability Predictive Value Probability Process Psychophysiology Pulse taking ROC Curve Range Research Research Personnel Resolution Role Running Safety Scanning Screening for cancer Screening procedure Sensitivity and Specificity Series Signal Transduction Specific qualifier value Standards of Weights and Measures 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 ideal observer (Bayesian)improved indexing insight instrument malignant breast neoplasm mathematical model novel strategies 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）：该项目旨在开发一种第一原理方法来设计和评估乳腺超声检查，专门用于癌症检测和恶性病变鉴别。该方法基于乳腺超声检查的贝叶斯理想观察者模型，该模型揭示了基于最大化特定检查的诊断信息内容的图像形成的最佳策略。我们当前对理想观测器策略的近似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点、大孔径阵列的数据。该项目提出的扩展包括迭代维纳滤波器，以将该方法扩展到高度非平稳的散射介质，以及开发预波束形成回波数据的理想观察器，从而可以优化特定临床任务的波束形成策略。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，与标准波束形成相比，可以显着提高病变可见度。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，最后用组织模拟模型进行测试来开发这种新的成像方法。这些研究可能会带来新的乳腺成像最佳技术，以及如何设计和评估所有超声成像技术的新视角。我们正在开发一种新的乳腺癌超声成像方法，该方法有望显着改善诊断。如果成功，超声检查将利用高效、安全和低成本的技术在癌症筛查中发挥更重要的作用。