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Minimize RSR Award Detail

Research Spending & Results

Award Detail

  • Hongcheng Liu
  • bo lu
Award Date:07/10/2020
Estimated Total Award Amount: $ 317,487
Funds Obligated to Date: $ 317,487
  • FY 2020=$317,487
Start Date:08/01/2020
End Date:07/31/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:High-Fidelity Radiotherapy Treatment Planning via Dimension-Free Zeroth-Order Algorithms
Federal Award ID Number:2016571
DUNS ID:969663814
Parent DUNS ID:159621697
Program:OE Operations Engineering
Program Officer:
  • Georgia-Ann Klutke
  • (703) 292-2443

Awardee Location

Awardee Cong. District:03

Primary Place of Performance

Organization Name:University of Florida
Street:1 University of Florida
Cong. District:03

Abstract at Time of Award

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.

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