A New Beam Selection Method for MLC-Based Robotic Radiotherapy

• 作者: Bin Wang C. M. Charlie Ma Jiajin Fan Jinsheng Li Jun Kang Juwu Wang
• 来源期刊: 医学物理学、临床工程、放射肿瘤学(英文)
• 学科: 医学
• 关键词: ROBOTIC RADIOTHERAPY BEAM SELECTION CYBERKNIFE
• 年，卷（期）: 2015,（2）
• 页码范围: 143-148
• 页数: 6页
• 分类号: R73

摘要

Purpose: The CyberKnife system equipped with multileaf collimator (MLC) has been shown promising in treatment-time reduction and plan-quality improvement, because of the enhanced coverage of larger lesions and the improved target conformity. In this study, we aim to develop an efficient non-coplanar beam selection program for CyberKnife-based IMRT. Method: The candidate beam set in this study consists of 94 non-coplanar beams, each defined by a vector connecting a CyberKnife node and a target point. Our goal is to choose an adequately small number of beams that will allow the generation of high quality IMRT plans. We use the beam coverage of patient-surface as a surrogate for the solution space of beamlet-based inverse planning. Based on body-surface coverage and beam-projection overlap on the surface, a beam-selection program was developed. To evaluate the effectiveness of the beam selection method, IMRT plans with the selected beams for different treatment sites were generated using the Varian Eclipse treatment planning system and compared with the IMRT plans with conventional coplanar beams. Results: Our program efficiently selected a subset of relatively small number of non-coplanar beams, while pre-serving the body-surface coverage and therefore the solution space for inverse planning optimization. For example, a set of 17 beams were selected for a pancreatic cancer case, covering 92.5% of the surface area which was covered by all the 94 candidate beams with the same field size. The IMRT plans with the selected beams show superior quality with dramatically improved critical structure sparing, as compared with the clinically approved IMRT plans. Conclusion: One can efficiently select effective sets of non-coplanar beams with our program, which allow the generation of high-quality plans for MLC-based robotic radiotherapy.