Forward and Inverse Treatment Planning Solutions for Small Animal Radiation Research

Inverse Treatment Planning;Radiation Oncology;Computer Science

Cho, Bong JoonKazanzides, Peter ;

Johns Hopkins University

关键词: Inverse Treatment Planning;    Radiation Oncology;    Computer Science;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/58687/CHO-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

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 often 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.