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因，预计到 2023 年，美国将有超过 60 万人因癌症死亡，癌症患者高死亡率的主要原因之一是癌症的晚期诊断。由于大多数癌症不会出现早期症状，因此，开发快速有效的靶向疗法来治疗癌症患者的需求尚未得到满足，为此，生物医学成像通过帮助获得重要的解剖学信息，成为临床制定癌症方案的关键组成部分。 ,癌症形成和扩散的结构和功能信息特别是利用耦合波物理学的混合成像方法，提供癌组织的定量信息以指导更好的诊断、分期和治疗计划。 （QPAT）使用短脉冲近红外光和超声传播数据来重建癌组织中的高保真光学特性，例如光吸收和散射剖面。然而，存在一些实际挑战，例如缺乏足够的数据集和声速的不确定性。在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起来，以解决 QPAT 中的挑战并提供高质量的重建。结果，它将有助于促进癌组织的准确靶向成像并改善临床结果，从而有助于实现美国卫生与公众服务部“保障和改善国家和全球健康状况和结果”的战略目标之一。此外，该项目将提供一个为本科生和研究生，特别是来自代表性不足群体的学生提供独特的跨学科研究和培训经验，并将促进生物医学成像领域数学家、统计学家和放射科医生之间的跨学科合作。该项目的科学目标是建立一个新的准确类别，快速、稳定和鲁棒的非线性重建方案，用于解决 QPAT 中出现的有限数据混合成像问题 为了实现这一目标，具体研究目标是（1）开发一种新的无梯度纳什游戏计算方案，用于数据补全和计算。识别光声层析成像中未知的声速和光能量密度；（2）建立新的无梯度优化方案，用于重建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纳什算法，以获得QPAT 中的稳定重建方法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演问题提供一种新的计算方法范式，从而产生计算成本低廉、稳定且卓越的重建。与现有的计算框架相比，因此将有利于有效检测癌症。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。