A New Computational Framework for Superior Image Reconstruction in Limited Data Quantitative Photoacoustic Tomography

有限数据定量光声断层扫描中卓越图像重建的新计算框架

• 批准号: 2309491
• 负责人: Souvik Roy
• 金额: $ 19万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309491&HistoricalAwards=false
• 关键词: New; Computational; Framework; Superior; Image

Cancer is the second leading cause of death in the USA, behind heart disease. In 2023, over 600,000 cancer deaths are projected to occur in the USA. One of the primary factors behind the high death rate for cancer patients is the late diagnosis of cancer, since most cancers do not present early symptoms. Thus, there is an unmet need to develop fast and effective targeted therapies for treating cancer patients. For this purpose, biomedical imaging is a crucial component for establishing clinical protocols in cancer by helping obtain important anatomical, structural, and functional information of cancer formation and spread. In particular hybrid imaging methods, which use physics of coupled waves, provide quantitative information of cancerous tissues to guide better diagnosis, staging, and treatment planning. One such hybrid imaging method is quantitative photoacoustic tomography (QPAT) that uses short-pulse near infrared light and ultrasound propagation data to reconstruct high-fidelity optical properties, like light absorption and scattering profiles, in cancerous tissues. However, several practical challenges, like lack of adequate datasets and uncertainty of sound speed in tissues, limit the quality of reconstructions with existing computational methods in quantitative photoacoustic tomography. This project brings together a novel combination of theoretical and computational methods in mathematical game theory and statistical sensitivity analysis to tackle the aforementioned challenges and provide high quality reconstructions in QPAT. As a result, it will help facilitate accurate targeted imaging of cancerous tissues and improve clinical outcomes, thereby contributing to one of the strategic goals of USA Heath and Human Services to “Safeguard and Improve National and Global Health Conditions and Outcomes”. Furthermore, this project will provide a unique interdisciplinary research and training experience for undergraduate and graduate students, especially from underrepresented groups, and will facilitate interdisciplinary collaboration between mathematicians, statisticians, and radiologists in the field of biomedical imaging.The scientific goal of this project is to build a new class of accurate, fast, stable and robust non-linear reconstruction schemes for solving limited data hybrid imaging problems arising in QPAT. For achieving this goal, the specific research objectives are to (1) develop a new gradient-free Nash games computational scheme for data completion and identification of unknown sound speed and optical energy density in photoacoustic tomography; (2) build a new gradient-free optimization scheme for reconstruction of optical parameters with high contrast and resolution; and (3) use statistical sensitivity analysis to stabilize and calibrate the Nash algorithm for obtaining a stable reconstruction method in QPAT. The computational framework will be validated using real-time photoacoustic data of mice specimens. The project also aims at providing a new paradigm in computational methods for limited data inverse problems that yields computationally inexpensive, stable and superior reconstructions in comparison to existing computational frameworks, and thus will be beneficial for effective detection of cancers.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.

癌症是美国第二大死亡原因，仅次于心脏病。 2023年，预计在美国有60万次癌症死亡。癌症患者高死亡率的主要因素之一是癌症的晚期诊断，因为大多数癌症都不出现早期症状。这是未满足的，需要开发快速有效的靶向疗法来治疗癌症患者。为此，生物医学成像是通过帮助获得重要的解剖，结构和功能信息来建立癌症临床方案的关键组成部分。特别是使用耦合波物理学的混合成像方法提供了癌变时间的定量信息，以指导更好的诊断，分期和治疗计划。一种这样的混合成像方法是定量光声断层扫描（QPAT），该光声断层扫描（QPAT）使用近红外光线和超声传播数据来重建了取消组织中的高保真光学特性，例如轻滥用和散射曲线。但是，一些实际的挑战，例如缺乏足够的数据集和尖端中声速的不确定性，在定量光声断层扫描中使用现有的计算方法限制重建的质量。该项目汇集了数学游戏理论和统计灵敏度分析中理论和计算方法的新型组合，以应对挑战，并在QPAT中提供高质量的重建。结果，它将有助于促进准确的有针对性的取消技巧成像并改善临床成果，从而为美国希思和公共服务的战略目标之一做出贡献，以“维护和改善国家和全球健康状况以及结果”。此外，该项目将为本科生和研究生提供独特的跨学科研究和培训经验。学生，尤其是来自人为不足的团体的学生，并将促进生物医学成像领域的数学家，统计学家和放光学家之间的跨学科合作。该项目的科学目标是建立一类新的准确，快速，稳定和强大的非线性构造方案，以解决有限的数据杂交数据杂交数据，以解决有限的数据杂交杂交Imaging Compary Compail Indarising ar Qpat。为了实现这一目标，具体的研究目标是（1）开发一种新的无梯度NASH游戏计算方案，用于数据完成和鉴定光声断层扫描中未知的声速和光学能量密度； （2）建立一个新的无梯度优化方案，以重建具有高对比度和分辨率的光学参数； （3）使用统计灵敏度分析来稳定和校准NASH算法，以在QPAT中获得稳定的重建方法。计算框架将使用小鼠标本的实时光声数据进行验证。该项目还旨在为有限数据的计算方法提供新的范式，与现有的计算框架相比，有限的数据逆问题可产生计算廉价，稳定和优越的重建，因此将有效地检测癌症。该奖项通过评估了NSF的法规范围，反映了通过评估的范围来审查其知识群体的支持，并概述了基金会的支持。