Small Animal Radiation Research (SARR) is of paramount importance for the advancement of human radiotherapy (RT) by serving as a critical counterpart to perform comprehensive preclinical studies on a large number of subjects under controlled experimental conditions at low costs. SARR relies on dedicated platforms to administer radiation dose to animals in a similar way as in the clinic. Current-generation SARR irradiators, developed in the past decade, have failed to keep pace with technology advancements in human RT. In stark contrast to modern RT treatments where novel anatomical and functional imaging, inverse treatment planning, and intensity modulated delivery techniques are routinely employed to precisely form an extremely conformal dose distribution to the tumor, the therapeutic form in current SARR systems resembles an obsolete form of human RT. This technology disparity has substantially impaired SARR study relevance to human RT, impeded explorations in RT research, and hindered rapid conduction of SARR studies. Towards addressing this problem, in response to PAR-15-075, this project will develop and translate a next-generation SARR platform through an academic-industrial partnership, joining medical physicists and radiobiologists at UT Southwestern Medical Center (UTSW) with engineering experts at Faxitron Bioptics LLC (Faxitron). The developed system will be substantially superior to the current state-of-the-art SARR platform due to its novel imaging methods (dual energy cone beam CT and PET), intensity modulated radiotherapy, and high computation and treatment delivery efficiency. These novel features are expected to improve SARR research relevance to human RT by delivering treatments of clinical quality, to support exploration in modern RT by offering technical freedom to realize novel imaging and therapy approaches, and to increase research efficiency by enhancing computational speed and workflow. We will perform studies with the following specific aims (SAs): SA1: Refine hardware design and construct the hardware system including mechanical, imaging, and therapy subsystems. SA2: Refine software design and develop an imaging and treatment planning system accompanied with the hardware platform. SA3: Perform comprehensive system tests, develop a translation plan, and demonstrate achieved advantages of the system via an animal study on image-guided intensity-modulated lung stereotactic body radiotherapy using rats. The innovation of this project includes novel technological capabilities enabled by the next-generation SARR platform, as well as coherent translation activities to deliver new capabilities to end- users. Project feasibility is ensured by extensive preliminary studies, and the research team integrating medical physicists and radiobiologists (UTSW) with strong clinical and research expertise and engineers (Faxitron) with extensive commercial product development experience. By filling the critical void between SARR and human RT, the developed system will become an essential component in preclinical research for the exploration of novel radiotherapeutic strategies with high relevance to human RT.

Public Health Relevance

Technology in current small animal radiation research (SARR) has significantly lagged behind that in human radiotherapy (RT), impairing SARR study relevance to human RT, impeding explorations in RT research, and hindering rapid conduction of SARR studies. This project will develop a next-generation SARR platform with novel imaging, planning, delivery, and computation techniques to fill this critical void between SARR and human RT. The developed system will become an essential component in preclinical research for the exploration of novel radiotherapeutic strategies with high relevance to human RT.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Method to Extend Research in Time (MERIT) Award (R37)
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Special Emphasis Panel (ZRG1)
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Obcemea, Ceferino H
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University of Texas Sw Medical Center Dallas
Schools of Medicine
United States
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Shen, Chenyang; Gonzalez, Yesenia; Chen, Liyuan et al. (2018) Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning. IEEE Trans Med Imaging 37:1430-1439