High-Fidelity Radiotherapy Treatment Planning via Dimension-Free Zeroth-Order Algorithms

通过无量纲零阶算法的高保真放射治疗计划

• 批准号: 2016571
• 负责人: Hongcheng Liu
• 金额: $ 31.75万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016571&HistoricalAwards=false
• 关键词: Fidelity; Radiotherapy; Treatment; Planning; via

This award will contribute to the Nation's health and welfare by improving methods for radiation therapy (radiotherapy) in the treatment of cancer. Radiotherapy has long been used as a prevalent mode of cancer treatment; its effectiveness lies in using high-energy radiation to eradicate cancer cells while sparing the surrounding normal tissue. Underlying the delivery of radiotherapy are complex optimization algorithms that determine safe and effective treatment plans. The creation of accurate treatment plans is difficult due to the high-dimensionality of the problems as well as to uncertainties in individual response to radiation dosage. This project develops methods to improve algorithms that guide the delivery of precise amounts of radiation to target cells. The research results will be integrated into a continuing medical education program to facilitate collaborations between academics and medical practitioners. To attract recent high school graduates, especially those from under-represented communities, into STEM majors, the project team will participate in the STEPUP outreach program at the University of Florida. This project aims to create fundamentally new zeroth-order algorithmic paradigms that are provably capable of mitigating the The research plan will study variations of randomized gradient-free algorithms that exploit computation-facilitating structures such as sparsity and its generalizations. The project will also derive and analyze algorithms that combine optimization and deep learning methods in solving problems without the knowledge of closed-form formulations. In theory, the computational efficiency of these algorithms is expected to be almost independent of problem dimensionality, up to a logarithmic term. These algorithms will be integrated with the Monte Carlo simulators deemed the gold standard in providing accurate modeling of radiotherapy outcomes. The resulting new treatment planning engines are expected to improve plan fidelity without increasing the computational cost. Extensive experiments and comparisons of the methods will be conducted on realistic cancer treatment data.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.

该奖项将通过改进癌症治疗中的放射治疗(放射治疗)方法，为国家的健康和福利做出贡献。长期以来，放射治疗一直被用作癌症治疗的一种普遍模式；其有效性在于使用高能辐射来根除癌细胞，同时保留周围正常组织。提供放射治疗的基础是确定安全有效的治疗计划的复杂优化算法。由于问题的高维度以及个体对辐射剂量反应的不确定性，制定准确的治疗计划是困难的。该项目开发了改进算法的方法，以指导将精确数量的辐射输送到目标细胞。研究成果将被整合到一个继续医学教育计划中，以促进学者和医生之间的合作。为了吸引应届高中毕业生，特别是那些来自代表性不足的社区的毕业生，进入STEM专业，该项目团队将参加佛罗里达大学的Stepup外联计划。该项目旨在从根本上创建新的零阶算法范例，这些范例能够被证明能够缓解气候变化。研究计划将研究利用稀疏性及其推广等便于计算的结构的随机化无梯度算法的变体。该项目还将推导和分析在解决问题时结合优化和深度学习方法的算法，而不需要了解封闭形式的公式。理论上，这些算法的计算效率预计几乎与问题维度无关，最高可达对数项。这些算法将与蒙特卡洛模拟器集成在一起，蒙特卡洛模拟器被认为是提供准确的放射治疗结果建模的金标准。由此产生的新的治疗计划引擎有望在不增加计算成本的情况下提高计划的保真度。这些方法将在现实的癌症治疗数据上进行广泛的实验和比较。这一奖项反映了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。

项目成果

High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks

• DOI: 10.1287/opre.2021.2217
• 发表时间: 2019-03
• 期刊: Oper. Res.
• 作者: Hongcheng Liu;Y. Ye;H. Lee

Training generalizable quantized deep neural nets

• DOI: 10.1016/j.eswa.2022.118736
• 发表时间: 2022-10
• 期刊: Expert Syst. Appl.
• 作者: Charles Hernandez;Bijan Taslimi;H. Lee;Hongcheng Liu;P. Pardalos

Regularized sample average approximation for high-dimensional stochastic optimization under low-rankness

• DOI: 10.1007/s10898-022-01206-3
• 发表时间: 2019-04
• 期刊: Journal of Global Optimization
• 影响因子: 1.8
• 作者: H. Lee;Charles Hernandez;Hongcheng Liu

A practical algorithm for VMAT optimization using column generation techniques

• DOI: 10.1002/mp.15776
• 发表时间: 2022-06-07
• 期刊: MEDICAL PHYSICS
• 影响因子: 3.8
• 作者: Wang，Yuanbo;Liu，Hongcheng;Lu，Bo
• 通讯作者: Lu，Bo

An ultra-fast deep-learning-based dose engine for prostate VMAT via knowledge distillation framework with limited patient data

基于有限患者数据的知识蒸馏框架，基于超快速深度学习的前列腺 VMAT 剂量引擎

• DOI: 10.1088/1361-6560/aca5eb
• 发表时间: 2022
• 期刊: Physics in Medicine & Biology
• 影响因子: 3.5
• 作者: Tseng, Wenchih;Liu, Hongcheng;Yang, Yu;Liu, Chihray;Lu, Bo
• 通讯作者: Lu, Bo