权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

用于癌症检测和治疗监测的 3D 超分辨率超声成像

基本信息

批准号：
10318580
负责人：
Kenneth Hoyt
金额：
$ 34.43万
依托单位：
UNIVERSITY OF TEXAS DALLAS
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10318580
关键词：
3-Dimensional Algorithms Animal Model Antineoplastic Agents Biological Markers Blood Vessels Breast Cancer Detection Breast Cancer Model Cancer Detection Cancerous breast Characteristics Clinical Clinical Research Computational algorithm Computer software Contrast Media Custom Detection Development Disease Disease Progression Early Diagnosis Early treatment Elements Exhibits Gaussian model Goals Growth Hour Human Imaging technology In complete remission Malignant Neoplasms Mammary Gland Parenchyma Mammary Neoplasms Measurement Methods Microbubbles Monitor Morphology Motion Neoadjuvant Therapy Neoplasm Metastasis Operative Surgical Procedures Organ Pathologic Patients Perfusion Physical shape Play Recurrence Regimen Research Research Project Grants Resolution Role Survival Rate System Three-Dimensional Image Three-dimensional analysis Time Tissues Transducers Transgenic Animals Tumor Angiogenesis Tumor stage Ultrasonography Vascular blood supply advanced breast cancer aggressive breast cancer anti-cancer therapeutic base cancer biomarkers cancer imaging cancer therapy chemotherapy clinical imaging clinical translation computerized data processing computing resources contrast imaging cost deep learning deep learning algorithm detection limit human disease human tissue image processing image reconstruction imaging modality imaging system improved in vivo innovation learning strategy malignant breast neoplasm millisecond novel open source portability programs response standard of care treatment response tumor ultrasound

项目摘要

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.

项目总结新辅助化疗是治疗局部晚期乳腺癌的标准护理，这是一种主要的临床问题。获得确定早期治疗反应的廉价和非侵入性方法的途径是对于确定选定的抗癌治疗方案是否有效至关重要。肿瘤血管生成是关键乳腺癌生长和转移的生物标志物。已知的肿瘤微血管显示出明显的乳腺肿瘤发生早期的血流灌注特征和形态特征在对新辅助治疗的积极反应期间发生根本性变化。这件事的首要目标是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，显著提高我们进行活体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扫描仪的功能换能器。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血流和微血管形态的运动校正和定量分析 3D空间中的要素。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记物的使用。超声图像作为区分转基因动物健康组织体积和病变组织体积的定量基础乳腺癌的模型。我们还将评估活体3D SR-US成像在早期肿瘤检测中的应用使用相同的动物模型对新辅助治疗的反应。