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.

在美国，癌症是仅次于心脏病的第二大死因。2023年，美国预计将有超过60万人死于癌症。癌症患者高死亡率背后的主要因素之一是癌症诊断较晚，因为大多数癌症没有早期症状。因此，开发快速有效的靶向治疗癌症患者的需求尚未得到满足。为此，生物医学成像是建立癌症临床方案的重要组成部分，有助于获得癌症形成和扩散的重要解剖、结构和功能信息。特别是混合成像方法，它利用耦合波的物理原理，提供了癌症组织的定量信息，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成像(QPAT)，它使用短脉冲近红外光和超声波传播数据来重建癌症组织中的高保真光学属性，如光吸收和散射轮廓。然而，一些实际的挑战，如缺乏足够的数据集和组织中声速的不确定性，限制了现有计算方法在定量光声断层成像中的重建质量。该项目将数学博弈论和统计敏感性分析中的理论和计算方法结合在一起，以应对上述挑战，并在QPAT中提供高质量的重建。因此，它将有助于促进对癌症组织进行准确的靶向成像，并改善临床结果，从而为美国卫生与公众服务部的战略目标之一做出贡献，即“保障和改善国家和全球的健康状况和结果”。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，特别是来自代表性不足的群体，并将促进数学家、统计学家和放射科医生在生物医学成像领域的跨学科合作。本项目的科学目标是建立一类新的准确、快速、稳定和稳健的非线性重建方案，以解决QPAT中出现的有限数据混合成像问题。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 Games计算方案，用于光声层析成像中未知声速和光能密度的数据补全和识别；(2)建立一种新的无梯度优化方案，用于高对比度和高分辨率的光学参数重建；(3)使用统计灵敏度分析来稳定和校准Nash算法，以获得稳定的QPAT重建方法。计算框架将使用小鼠样本的实时光声数据进行验证。该项目还旨在为有限数据反问题提供一种新的计算方法范例，与现有的计算框架相比，该方法产生的计算成本更低、稳定和更优越的重建，从而将有助于有效地检测癌症。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。