AF: Small: Algorithmic Problems in Applied Computational Geometry

AF：小：应用计算几何中的算法问题

• 批准号: 0916606
• 负责人: Danny Chen
• 金额: $ 44万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916606&HistoricalAwards=false
• 关键词: AF; Small; Algorithmic; Problems; Applied

Computer technologies play an important role in modern medicine and lifesciences, especially in diagnostic imaging, human genome study, treatmentoptimization, and medical data management. Many computational problems arising in the field of biomedicine call for efficient and good quality algorithmic solutions. This project aims to develop new geometric computing and algorithmic techniques for solving computational problems in biomedical and engineering applications.This research investigates a number of geometric optimization problems that are theoretically challenging and practically relevant. The target problems belong to fundamental topics of computational geometry, such as geometric partition, covering, shaping, approximation, motion planning, and clustering; they also arise in important applied areas such as radiation cancer treatment, medical imaging, biology, computer-aided manufacturing, and data mining. Some of the algorithms and software developed during the preliminary studies of this research have produced significantly better solutions for real application problems (for example, much improved radiation cancer therapy plans over those computed by thecurrent commercial radiation treatment planning systems). The research will draw diverse techniques from other theoretical areas such as graph algorithms, combinatorial optimization, discrete mathematics, and operations research. It will also provide a rich source of interesting new problems/questions and new ideas to prod further development of algorithmic techniques in computational geometry and other theoretical areas. This project is expected to generate broader impacts beyond computational geometry and even computer science. It will produce efficient and effective algorithms and software for solving key problems in radiation cancer therapy and surgery, medical imaging, biology, and other applied areas. Furthermore, the newly developed algorithms and software will be incorporated into practical applications such as clinical radiation cancer treatment systems. Hence, this research will help unite and integrate the power of computational geometry, computer algorithms, and modern biomedicine for diagnostic imaging, radiation cancer treatment, and other applications, and improve the quality of life for the patients.

计算机技术在现代医学和生命科学中发挥着重要作用，特别是在诊断成像、人类基因组研究、治疗优化和医疗数据管理方面。 在生物医学领域中出现的许多计算问题需要高效和高质量的算法解决方案。 本研究旨在开发新的几何计算和算法技术，以解决生物医学和工程应用中的计算问题。本研究探讨了一些理论上具有挑战性和实际相关性的几何优化问题。 目标问题属于计算几何的基本主题，如几何划分，覆盖，成形，近似，运动规划和聚类;它们也出现在重要的应用领域，如放射性癌症治疗，医学成像，生物学，计算机辅助制造和数据挖掘。 在本研究的初步研究期间开发的一些算法和软件已经为真实的应用问题产生了显著更好的解决方案（例如，与当前商业放射治疗计划系统计算的那些相比，大大改进的放射癌症治疗计划）。 该研究将从其他理论领域，如图算法，组合优化，离散数学和运筹学中汲取不同的技术。 它还将提供一个有趣的新问题/问题和新思路的丰富来源，以推动计算几何和其他理论领域的算法技术的进一步发展。 该项目预计将产生更广泛的影响，超越计算几何，甚至计算机科学。它将产生高效和有效的算法和软件，用于解决放射癌症治疗和手术，医学成像，生物学和其他应用领域的关键问题。 此外，新开发的算法和软件将被纳入临床放射癌症治疗系统等实际应用中。 因此，这项研究将有助于统一和整合计算几何，计算机算法和现代生物医学的力量，用于诊断成像，放射癌症治疗和其他应用，并提高患者的生活质量。