LEAPS-MPS: Deep Quasi-Reversibility Inversion for Source Localization Oncological Problems

LEAPS-MPS：源定位肿瘤问题的深度准可逆反演

• 批准号: 2316603
• 负责人: Anh Khoa Vo
• 金额: $ 7.83万
• 依托单位: Florida Agricultural and Mechanical University
• 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: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316603&HistoricalAwards=false
• 关键词: LEAPS; MPS; Deep; Quasi; Reversibility

Brain tumors are among the most fatal cancers, affecting millions of individuals worldwide. In the United States alone, each year thousands of adults and children receive a primary brain tumor diagnosis. A key focus for oncologists, neurologists, and other scientists involved in the field is in determining the specific anatomical origin site of the tumor. Knowing this site might help in gaining insights in how the tumor behaves, in predicting the symptoms it is likely to cause, and in identifying genetic syndromes that have a high association with brain tumors. Therefore, this source information can be an important aid in the early detection of brain cancer. This project studies a reliable and efficient inversion algorithm called Deep Quasi-Reversibility Method (DQRM) that can quickly reconstruct the primary tumor’s location. By actively involving undergraduate students in research activities and fostering collaborations between institutions, the project will contribute to the well-being of individuals affected by brain tumors as well as help to cultivate a skilled STEM workforce. The project is based at a long-established HBCU, thus providing an opportunity to broadening research participating in STEM.The project involves numerical and theoretical studies of the proposed DQRM for solving the source localization oncological question with different levels of complexity. The DQRM is a combination of a variational quasi-reversibility (QR) method and a deep learning mesh-free-based algorithm. The design brings together techniques of computational mathematics, partial differential equations, and machine learning to fast deliver a reliable and accurate quasi-solution. On one hand, the variational QR approach can overcome localized features, highly dynamic nonlinearities, and the inherent exponential instability of the reconstruction process. On the other, the deep learning approach handles the curse of dimensionality and the costs associated with data measurement. The first objective of the project is to study the effectiveness of the inverse solver in tackling the quasi-linear parabolic models associated with the evolutionary dynamics of tumor cells. The second is to investigate the applicability of the algorithm by incorporating an advanced tumor growth model that considers factors such as age, size, and spatial structure. The theoretical theme is centered around the convergence of a neural network approximator towards the quasi-solution.This project is funded in part by the Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) program.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.

脑肿瘤是最致命的癌症之一，影响了全球数百万个人。仅在美国，每年，成千上万的成年人和儿童都会接受原发性脑肿瘤诊断。肿瘤学家，神经科医生和其他参与该领域的科学家的重点是确定肿瘤的特定解剖学位点。了解该部位可能有助于获得有关肿瘤行为，预测可能引起的症状以及鉴定与脑肿瘤较高相关的遗传综合征的见解。因此，此源信息可能是早期发现脑癌的重要帮助。该项目研究了一种可靠，有效的反转算法，称为深质可逆方法（DQRM），可以快速重建原发性肿瘤的位置。通过积极参与研究活动并促进机构之间的合作，该项目将有助于受脑肿瘤影响的个人的福祉，并有助于培养熟练的STEM劳动力。该项目基于长期建立的HBCU，因此为扩大参与STEM的研究提供了机会。该项目涉及所提出的DQRM的数值和理论研究，用于解决源本地化肿瘤学问题，其复杂性不同。 DQRM是多样性准可逆性（QR）方法和基于深度学习网格的算法的组合。该设计将计算数学，部分微分方程和机器学习的技术汇集在一起​​，以快速提供可靠，准确的准溶液。一方面，变异QR方法可以克服局部特征，高度动态的非线性以及重建过程的指数不稳定性。另一方面，深度学习方法处理维度曲线以及与数据测量相关的成本。该项目的第一个目的是研究反求解器在应对与肿瘤细胞进化动力学相关的准线性抛物线模型方面的有效性。第二个是通过编码考虑年龄，大小和空间结构等因素的晚期肿瘤生长模型来研究算法的适用性。 The theoretical theme is centered around the convergence of a neural network Approximately towards the quasi-solution.This project is funded in part by the Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) program.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.