This project aims to develop an innovative physical approach to accurately predict the location of a moving lung tumor during streotactic body radiotherapy (SBRT). The strategy involves using four-dimensional computed tomography (4DCT) to calibrate a tumor base-model at simulation and using video-based optical surface imaging (4DSI) to provide motion adaptation to infer tumor position during treatment. This tumor motion model accounts for breathing irregularities and baseline drift, which cannot be handled by any existing correlation-based motion surrogate. Therefore, the proposed method potentially provides an accurate, reliable, and non-radiological means of monitoring a moving tumor for motion-tracking SBRT. Previously, Dr. Li discovered the physical relationships of torso-volume change with dynamic tidal volume and mean diaphragm motion. He has collaborated with Dr. Wei for 1.5 years and published 2 papers on lung motion characterization and breathing periodicity, facilitated by Dr. Wei's expertise in computer vision and machine learning. Preliminary data have been generated, including accuracy assessment of 4DSI in volume measurement, framework for a tumor motion perturbation model, and characterization of lung ventilation and motion. We therefore hypothesize that this physical law-based method will provide an accurate estimation of tumor location, thereby potentially improving radiation therapy for cancer. In particular, we propose to establish a tumor motion perturbation model using both 4DCT and 4DSI. We will characterize the physical relationship between a moving tumor and the bronchial tree, chest wall, diaphragm, and torso surface (see Specific Aim 1). We will build a motion base model, apply 4DSI for spatial and temporal inputs, and finalize the motion perturbation model. We also propose to establish 4DSI-based spirometry and clinically validate the proposed method, through both volunteer and patient studies (see Specific Aim 2). We will establish an IRB protocol for patient studies using concurrent 4DSI and fluoroscopy to validate the tumor motion model. We will also study volunteer respiration using 4DMR and spirometry. This pilot project will demonstrate the proof of principle that this novel approach can accurately target lung tumors in 4D image-guided SBRT.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA132378-07
Application #
8744976
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
7
Fiscal Year
2014
Total Cost
Indirect Cost
Name
City College of New York
Department
Type
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10031
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