CRII: SCH: Multi-modal Soft Tissue Characterization for Non-invasive Breast Imaging

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。乳腺癌是全世界女性中最常见的癌症类型，其早期发现是改善治疗后预后的最重要因素之一，这显著影响幸存者的生活质量。该项目将利用超声和电阻抗断层扫描的优化框架，对软组织行为进行基本理解，为乳腺癌筛查提供一种可访问且可靠的潜在用途工具。人类乳腺中胶原蛋白的独特特征将为新的乳腺癌生物标志物提供线索。该项目的成功完成将带来与先进的乳腺癌筛查技术相关的技术改进，该技术采用多模态特征，如可穿戴传感器、医学成像中的人工智能和即时诊断系统。研究小组将实现一个优化框架，该框架可以从软组织的物理反应中学习，以便使用超声和电阻抗断层成像相结合的集成成像技术无创地可视化人体乳房的横切面。第一个具体目标是建立优化算法和多模态参数，为软组织对机电刺激的积极响应提供一条分界线，并使伪影最小。所得的图像将说明软组织如何响应超声刺激下的不同模式的电场与温度分布信息在整个软组织。第二个具体目标是开展涉及健康受试者的重复测量研究，以确定随时间推移影响多模态表征结果的关键因素。这项观察性研究不仅将提高对算法参数的理解，这些参数需要考虑算法的患者特异性适应的时间变异性，同时弥合人类乳腺组织的离体和体内机电特性之间的差距。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Breast cancer is the most common type of cancer among women worldwide, and its early detection is one of the most important enabling factors for better post-treatment prognosis which significantly affects the quality of life for survivors. This project will develop a fundamental understanding of soft tissue behaviors using an optimization framework with ultrasound and electrical impedance tomography to provide an accessible and reliable tool for potential usages in breast cancer screening. The specification of a distinct feature of collagen in the human breast will provide a clue for a new breast cancer biomarker. The successful completion of the project will lead to improved technologies associated with an advanced breast cancer screening technique employing multi-modal characterization such as wearable sensors, artificial intelligence in medical imaging, and point-of-care diagnostic systems.The research team will implement an optimization framework that can learn from the physical responses of soft tissue in order to visualize the cross-sectional human breast non-invasively using an integrated imaging technique combining ultrasound and electrical impedance tomography. The first specific aim is to establish the optimization algorithms and multi-modal parameters that present a line of demarcation for actively responding soft tissues against the electro-mechanical stimuli with minimal artifacts. The resultant images will illustrate how the soft tissues respond against ultrasound stimulation under different patterns of electric fields with temperature distribution information across the soft tissues. The second specific aim is to conduct a repeated measure study involving healthy subjects so as to identify critical factors that affect the multi-modal characterization results over time. This observational study will lead to not only an improved understanding of the algorithm parameters required to account for temporal variability for patient-specific adaptation of the algorithm while bridging the gap between ex vivo and in vivo electromechanical characteristics of human breast tissues.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.