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

CRII：SCH：非侵入性乳腺成像的多模式软组织表征

• 批准号: 2153430
• 负责人: Kihan Park
• 金额: $ 17.5万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153430&HistoricalAwards=false
• 关键词: CRII; SCH; Multi; modal; Soft

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.

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。乳腺癌是全球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，显著影响幸存者的生活质量。该项目将使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了解，为乳腺癌筛查的潜在用途提供一种可访问和可靠的工具。人类乳腺中胶原蛋白的独特特征的说明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导致与先进的乳腺癌筛查技术相关的技术得到改进，该技术采用多模态表征，如可穿戴传感器，医学成像中的人工智能，研究小组将实施一个优化框架，该框架可以从软组织的物理反应中学习，以便可视化人体乳房的横截面，使用结合超声和电阻抗断层扫描的集成成像技术侵入性地进行。第一个具体目标是建立优化算法和多模态参数，这些优化算法和多模态参数呈现出一条分界线，用于以最小的伪影对机电刺激主动响应软组织。所得图像将说明软组织如何在不同电场模式下对超声刺激作出反应，并提供软组织上的温度分布信息。第二个具体目标是进行一项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解释患者时间变异性所需的算法参数的理解，该奖项反映了NSF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进行评估，被认为值得支持的搜索.