Interfraction anatomic changes and intrafraction respiratory motion are the major limiting factors for escalating radiation dose and improving local control in lung cancer radiotherapy. The advent of on-board x-ray imaging device mounted on the medical linear accelerator (LINAC) has provided a tool to obtain valuable anatomic information of the patient in the treatment position. However, due to the slow rotating nature of the on-board imaging system (~1 min per rotation), obtaining volumetric information in real time is extremely challenging. Existing methods have relied on grouping many projections acquired over multiple breathing cycles for several minutes to reconstruct one static anatomy. Further, due to the fact that lung cancer patients tend to breathe irregularly, the reconstructed images are often heavily contaminated by breathing motion artifacts. The goal of this research project is to develop innovative real-time volumetric imaging methods that are able to reconstruct the dynamic patient anatomy in real time (~0.1 s) using a single x-ray projection during dose delivery. This bold goal is made practical by three integral components: effective use of an accurate patient-specific lung motion model, advanced compressed sensing techniques for image reconstruction, and a massively parallel and yet affordable computing platform based on graphics processing units (GPU). During the mentored K99 phase, the candidate will draw on his signal processing and statistical modeling expertise to improve and optimize the patient-specific lung motion model while gaining knowledge in lung patient anatomy and pathology, and to quantitatively evaluate the lung motion model and interpret the clinical significance of the results. During the independent R00 phase, a real-time volumetric imaging method which captures both interfraction anatomical changes and intrafraction breathing motion, will be developed, implemented, and evaluated through systematic phantom and patient studies. Successful completion of this project will overcome a critical barrier to the urgently needed real-time volumetric image guidance in lung cancer radiotherapy and afford a powerful way for us to safely escalate the radiation dose and improve local control of lung cancer. This project fits perfectly with the candidates long-term career goal of establishing a high-quality independent research program to develop state-of-the-art x-ray imaging techniques, which will provide real-time image guidance for cancer radiotherapy and ultimately improve the therapeutic ratio and enhance the quality of life for cancer patients. Career development and research training will be an integral component during the mentored phase of this project. This training will be further supplemented with formal coursework at Stanford University School of Medicine, as well as participation in research seminars and scientific meetings. The training and research contributions supported by this K99/R00 award will substantially enhance the candidates career and serve to establish him as a successful independent investigator in the near future.

Public Health Relevance

This project aims to develop real-time volumetric imaging methods that are able to reconstruct the real-time dynamic patient anatomy using a single x-ray projection during dose delivery. Successful completion of the project will overcome a critical barrier to the urgently needed real-time volumetric image guidance in lung cancer radiotherapy. It will provide a critically needed means to treat lung cancer and afford a powerful way for us to safely escalate the radiation dose and improve local control of lung cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Transition Award (R00)
Project #
4R00CA166186-03
Application #
8901581
Study Section
Special Emphasis Panel (NSS)
Program Officer
Capala, Jacek
Project Start
2012-08-02
Project End
2017-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
$241,530
Indirect Cost
$91,044
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Gudur, Madhu Sudhan Reddy; Hara, Wendy; Le, Quynh-Thu et al. (2014) A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Phys Med Biol 59:6595-606