The goal of this project is to establish a novel paradigm of dense angularly sampled and sparse intensity- modulated radiation therapy (DASSIM-RT). In this scheme, the redundant or dispensable modulation of the incident intensity-modulated beams is removed effectively by using a compressed sensing (CS) technique. The delivery time saved in this way is used to increase the angular sampling for improved dose conformality. By balancing the angular sampling and intensity modulation, DASSIM-RT enables us to fully utilize the technical capabilities of modern digital linacs to produce highly conformal dose distributions that can be delivered efficiently. Specifically, we wil 1) set up a compressed sensing (CS)-based framework for inverse treatment planning; 2) investigate a new type of treatment scheme termed DASSIM-RT; and 3) show the advantage of DASSIM-RT through a series of phantom cases and previously treated patients. DASSIM-RT represents a truly optimal RT scheme with uncompromised angular sampling (including non-coplanar beams), beam intensity modulation, and possible field-specific energy and collimator angle. If successful, the project will allow us to overcome many of the limitations of existing treatment schemes to meet the unmet clinical demand for highly conformal dose distributions in radiation oncology.

Public Health Relevance

This project is directed at establishing a dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) scheme to advance RT treatment techniques to a new paradigm. The project will develop enabling concepts and technologies including: compressed sensing-based inverse planning, DASSIM-RT, single arc delivery scheme of DASSIM-RT, and prior knowledge guided search of optimal beam configuration. The proposed research promises to overcome many of the limitations of existing treatment schemes and empower the radiation oncology discipline with substantially improved tools for cancer management.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA176553-05
Application #
9488420
Study Section
Radiation Therapeutics and Biology Study Section (RTB)
Program Officer
Obcemea, Ceferino H
Project Start
2014-06-01
Project End
2019-05-31
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
5
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Ibragimov, Bulat; Toesca, Diego; Chang, Daniel et al. (2018) Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys 45:4763-4774
Mo?nik, Domen; Ibragimov, Bulat; Xing, Lei et al. (2018) Segmentation of parotid glands from registered CT and MR images. Phys Med 52:33-41
Liu, Hongcheng; Xing, Lei (2018) Isodose feature-preserving voxelization (IFPV) for radiation therapy treatment planning. Med Phys 45:3321-3329
Qin, Wenjian; Wu, Jia; Han, Fei et al. (2018) Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Phys Med Biol 63:095017
Ren, Shangjie; Hara, Wendy; Wang, Lei et al. (2017) Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. Int J Radiat Oncol Biol Phys 97:849-857
Wang, Huan; Dong, Peng; Liu, Hongcheng et al. (2017) Development of an autonomous treatment planning strategy for radiation therapy with effective use of population-based prior data. Med Phys 44:389-396
Ibragimov, Bulat; Xing, Lei (2017) Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys 44:547-557
Ibragimov, Bulat; Toesca, Diego; Chang, Daniel et al. (2017) Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol 62:8943-8958
Ar?k, Sercan Ö; Ibragimov, Bulat; Xing, Lei (2017) Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham) 4:014501
Han, Bin; Ding, Aiping; Lu, Minghui et al. (2017) Pixel response-based EPID dosimetry for patient specific QA. J Appl Clin Med Phys 18:9-17

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