The overall hypothesis of this grant is that the local control of lung cancer can be significantly improved, at fixed or reduced morbidity levels, by advances in three- dimensional imaging, treatment planning, and delivery which overcome dose delivery errors due to breathing motion. A critical component of this goal is the quantitative understanding of how the lung and lung tumor tissues move as the patient breathes. Treatment planners have to increase the size of the radiation portal to account for tumor motion, engage the radiation beam only when the tumor is beneath the portal, or track the portal with the tumor motion. Each of these strategies requires that the tumor and other lung tissue positions be accurately known. Our group has developed a novel mathematical lung tissue and lung tumor motion (trajectory) model that relates the tissue positions to the breathing depth and rate of breathing.
Specific aim 1 will develop and validate an image deformation algorithm, coupled with computed tomography imaging and reconstruction methods, to provide optimal input data for the breathing motion model.
Specific aim 2 will show that our motion model does not change appreciably for patients that do not have their lungs irradiated.
Specific aim 3 will show that, for lung cancer radiation therapy patients, the lung motion model will change during therapy. We will evaluate the changes and hypothesize that the change can be modeled during the course of therapy, allowing the treatment planner to plan for future changes (adaptation) rather than assume that no change takes place. In addition to radiation therapy, the breathing motion model has potential applications in nuclear medicine imaging and lung physiology research.

Public Health Relevance

We are developing a mathematical description of human breathing motion for use in radiation treatments of lung cancer. The description uses imaging data acquired using sophisticated computed tomography (CT) scans that are acquired while the patient's breathing is monitored. The CT acquisition and analysis methods will be developed to maximize the model performance and the performance will be measured in lung-cancer and non-lung cancer radiation therapy patients. If the motion model is found to be accurate, it may have applications outside of radiation therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA096679-08
Application #
8289339
Study Section
Radiation Therapeutics and Biology Study Section (RTB)
Program Officer
Deye, James
Project Start
2002-07-01
Project End
2014-12-31
Budget Start
2012-01-01
Budget End
2013-12-31
Support Year
8
Fiscal Year
2012
Total Cost
$319,301
Indirect Cost
$111,963
Name
University of California Los Angeles
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
White, Benjamin M; Santhanam, Anand; Thomas, David et al. (2014) Modeling and incorporating cardiac-induced lung tissue motion in a breathing motion model. Med Phys 41:043501
Thomas, David; Lamb, James; White, Benjamin et al. (2014) A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases. Int J Radiat Oncol Biol Phys 89:191-8
White, Benjamin M; Zhao, Tianyu; Lamb, James et al. (2013) Quantification of the thorax-to-abdomen breathing ratio for breathing motion modeling. Med Phys 40:063502
White, Benjamin; Zhao, Tianyu; Lamb, James et al. (2013) Distribution of lung tissue hysteresis during free breathing. Med Phys 40:043501
Jani, Shyam S; Robinson, Clifford G; Dahlbom, Magnus et al. (2013) A comparison of amplitude-based and phase-based positron emission tomography gating algorithms for segmentation of internal target volumes of tumors subject to respiratory motion. Int J Radiat Oncol Biol Phys 87:562-9
Lamb, James M; Robinson, Clifford G; Bradley, Jeffrey D et al. (2013) Motion-specific internal target volumes for FDG-avid mediastinal and hilar lymph nodes. Radiother Oncol 109:112-6
Low, Daniel A; White, Benjamin M; Lee, Percy P et al. (2013) A novel CT acquisition and analysis technique for breathing motion modeling. Phys Med Biol 58:L31-6
Smith, Ryan L; Yang, Deshan; Lee, Andrew et al. (2011) The correlation of tissue motion within the lung: implications on fiducial based treatments. Med Phys 38:5992-7
Zhao, Tianyu; White, Benjamin; Moore, Kevin L et al. (2011) Biomechanical interpretation of a free-breathing lung motion model. Phys Med Biol 56:7523-40
Lamb, J M; Robinson, C; Bradley, J et al. (2011) Generating lung tumor internal target volumes from 4D-PET maximum intensity projections. Med Phys 38:5732-7

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