Tumor Diagnosis through Enhanced Ultrasound Imaging

简介(由申请人提供)：。建议的长远目标 研究计划是开发和提炼一种全新的超声波 组织微结构量化增强的成像方法 通过声学后向散射。增强的成像技术将被 适用于实体瘤的原位临床诊断 对癌症的准确检测和诊断的期望。 使用增强超声成像具有重要的医学意义，因为它 提供了一种快速和非侵入性的方法来检测和分类肿瘤类型。 为了实现这一长期目标的开始阶段，以下是 提出了具体目标： 1.对组织微结构的大小、形状和声学特性的假设 将使用不同的形状与当前的关联模型进行测试 具有非线性频率依赖性的因素。 2.考虑频率相关衰减损耗的新算法 组织将被测试并合并到后向散射测量中 改进了对组织微结构的估计。 3.将组织结构与模型参数相关联的技术将是 针对快速评估进行了优化。 4.建立接种肿瘤大鼠的对照组，建立 组织散射样本数据库。 5.将组织学数据与散布样本的量化联系起来 研究并用于检验超声散射模型的有效性。 6.几种将典型超声图像与 组织微结构的量化将被评估为促进 疾病的临床检测和诊断。

Description (provided by applicant): .The long term goal of the proposed research program is to develop and refine a fundamentally new ultrasound imaging approach enhanced by the quantification of tissue microstructure through acoustic backscatter. The enhanced imaging technique will then be adapted for use in an in situ clinical diagnosis of solid tumors with the expectation of accurate detection and diagnosis of cancer. Use of the enhanced ultrasound imaging is medically significant because it offers a quick and noninvasive means of detecting and classifying tumor types. To accomplish the beginning stages of this long-term goal, the following specific aims are proposed: 1. Assumptions of size, shape and acoustic properties of tissue microstructures are to be tested against current correlation models using different shape factors with nonlinear frequency dependence. 2. New algorithms that account for frequency-dependent attenuation losses in tissues will be tested and incorporated into backscatter measurements for improved estimation of tissue microstructures. 3. Techniques for relating tissue structures to model parameters will be optimized for rapid estimation. 4. A control group of rats seeded with tumors will be created to build a database of tissue scattering samples. 5. Relating histological data to quantification of scattering samples will be investigated and used to test the validity of the ultrasound scattering models. 6. Several techniques for combining typical ultrasound images with the quantification of tissue microstructures will be evaluated for facilitation of clinical detection and diagnosis of disease.