3D Super-Resolution Ultrasound Imaging for Cancer Detection and Treatment Monitoring

项目摘要 新辅助化疗是治疗局部晚期乳腺癌的标准治疗， 重大临床问题。获得廉价和非侵入性的方法来确定早期治疗反应是 这对于确定所选抗癌治疗方案是否有效至关重要。肿瘤血管生成是 乳腺癌生长和转移的生物标志物。已知这种肿瘤微血管分布表现出明显的 灌注特征和形态学特征在乳腺肿瘤发展的早期阶段， 在对新辅助治疗的积极反应期间发生根本性变化。这个项目的首要目标是 一个研究项目是开发一种创新的三维（3D）超分辨率（SR-US）成像系统 和新的图像处理解决方案，大大提高了我们进行体内定量分析的能力 肿瘤血管生成网络。该项目的第一个目标是优化3D SR-US成像 配备定制1024元素（32 x 32）矩阵阵列的可编程US扫描仪上的功能 传感器第二个目标是开发新的开源SR-US图像处理软件 用于执行肿瘤灌注和微血管形态的运动校正和定量分析 3D空间中的特征。在第三个目标中，我们将评估从3D SR中提取的血管生成生物标志物的使用， US图像作为区分转基因动物健康与患病组织体积的定量基础 乳腺癌的模型。我们还将评估使用体内3D SR-US成像检测早期肿瘤 使用相同的动物模型对新辅助治疗的反应。

PROJECT SUMMARY Neoadjuvant chemotherapy is the standard of care for treatment of locally advanced breast cancer, which is a major clinical issue. Access to inexpensive and noninvasive methods to determine early treatment response are essential to determine if a chosen anticancer therapeutic regimen is efficacious. Tumor angiogenesis is a key biomarker of breast cancer growth and metastasis. This tumor microvascularity is known to exhibit distinct perfusion characteristics and morphologic features during the early stages of breast tumor development which fundamentally change during a positive response to neoadjuvant treatment. The overarching goal of this research project is to develop an innovative three-dimensional (3D) super-resolution (SR-US) imaging system and new image processing solutions to considerably improve our ability to perform in vivo quantitative analysis of tumor angiogenic networks. The first aim of this project involves optimization of 3D SR-US imaging functionality on a programmable US scanner equipped with a custom 1024-element (32 x 32) matrix array transducer. The second aim involves the development of new open-source SR-US image processing software for performing motion correction and quantitative analysis of tumor perfusion and microvascular morphology features in 3D space. In the third aim, we will evaluate the use of angiogenic biomarkers extracted from 3D SR- US images as a quantitative basis for distinguishing healthy from diseased tissue volumes in a transgenic animal model of breast cancer. We will also assess the use of in vivo 3D SR-US imaging for detection of early tumor response to neoadjuvant treatment using the same animal model.