Pre-clinical irradiation systems have been developed and commercialized to minimize the gap between human clinical systems and small animal research systems. One of the examples is the small animal radiation research platform (SARRP) developed at Johns Hopkins University. Along with the development of pre-clinical irradiation systems, the need to understand the amount of dose deposited to different tissue types and to visualize the resulting dose distribution have motivated the development of treatment planning systems (TPS). Treatment planning in radiation therapy is often performed with a forward strategy to reuse an old treatment setup. However, this forward strategy cannot produce satisfactory results for complicated anatomical situations. This led to using an inverse strategy, but it is mathematically challenging and computationally demanding to define and solve the optimization problem.
This thesis provides an overview of the development of the SARRP TPS followed by a validation of the dose engine integrated into the SARRP TPS. Then, various inverse planning solutions are proposed to support complex treatment planning and delivery in small animal radiation research, which is the main topic and contribution of this thesis.
The first inverse planning contribution is an efficient two-dimensional (2D) uniform dose painting method using a motorized variable collimator (MVC), which is a compromise between the multileaf collimator (MLC) available in most human clinical systems and the size limitations of small animal radiotherapy systems. The second contribution is a fast 3D inverse planning framework that takes advantage of an existing GPU-accelerated superposition-convolution dose computation algorithm. It optimizes both beam directions and beam weights from a large number of initial beams in less than 5 minutes with a typical cone beam computed tomography (CBCT) image. The third contribution is an improvement on previous work for dose shell delivery, which includes a hollow cylinder as a planning and motion primitive. Specifically, this thesis improves the robot motion primitive for delivering a cylindrical beam, commissions the new primitive, and integrates the method into the SARRP TPS. The final contribution is the use of a statistical shape model (SSM) to enable further reductions in inverse planning time by providing a good guess for the initial beam arrangement.
Speaker Biography
Nathan (Bong Joon) Cho was raised in South Korea and received the Bachelor of Science degree in School of Computing from Soongsil University in 2006. He came to United States for the graduate studies in 2006 and received the Master of Science in Engineering in Computer Science from Johns Hopkins University in 2008. From that point on, he worked on several MRI-guided interventions as an assistant research engineer in the Laboratory for Computational Sensing and Robotics (LCSR). In Spring 2012, he started his Ph.D. program in Computer Science at Johns Hopkins University under the guidance of Dr. Peter Kazanzides. His Ph.D. research is on the development and validation of algorithms for complex dose planning and treatment delivery solutions based on the treatment planning system (TPS) of the Small Animal Radiation Research Platform (SARRP).