Fast Individualized Delivery Adaptation in Proton Therapy

质子治疗中的快速个体化治疗适应

• 批准号: 9893838
• 负责人: HARALD PAGANETTI
• 金额: $ 30.19万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9893838
• 关键词: Address; Algorithms; Background Radiation; Clinical; Communities; Computer software; Development; Dose; Gamma Rays; Geometry; Goals; Image; Measures; Methodology; Methods; Patients; Photons; Physics; Positioning Attribute; Proton Radiation; Protons; Radiation therapy; Research; Software Tools; Spectrum Analysis; System; Technology; Time; Uncertainty; Validation; X-Ray Computed Tomography; base; clinical practice; clinically significant; cone-beam computed tomography; image registration; improved; in vivo; innovation; novel; proton therapy; quality assurance; radiation delivery; treatment planning

While adaptive therapy has been studied before, none of the previous studies deals with the unique challenges (range uncertainties) and unique capabilities (beamlet optimization and prompt gamma imaging) of proton therapy. This proposal will for the first time address these aspects by developing innovative hardware and software methodologies. We envision treatment planning and delivery to be fully adaptive in terms of intra- fractional changes in patient geometry. This proposal aims at predicting the dose distribution (or a surrogate thereof) in the patient immediately prior to treatment delivery and correct for any discrepancies between the measured and intended dose in less than 2 minutes. This will enable us to deliver (proton) radiation therapy in an adaptive setting and much reduced target volume margins (2mm isotropic plus 2mm range margin in proton therapy in the beam direction) daily while the patient is positioned on the treatment table. We propose to achieve this goal by simultaneously developing fast hardware and software tools that take advantage of in-room prompt gamma and cone-beam CT imaging in combination with fast dose calculation. We will combine this technology with a novel framework on beamlet adaptation. While some of our methods will improve photon therapy as well, we will focus on proton therapy because it offers unique opportunities to dose verification in vivo as well as unique challenges due to range uncertainties. Our methodology will be made available to the entire proton therapy community.

虽然适应性治疗以前已经研究过，但以前的研究都没有涉及独特的挑战 （范围不确定性）和独特的能力（子束优化和即时伽马成像） 疗法该提案将首次通过开发创新硬件解决这些问题， 软件方法学。我们设想治疗计划和实施在内部方面完全自适应， 患者几何形状的部分变化。 该提议旨在预测在施用前即刻患者中的剂量分布（或其替代物）。 治疗输送，并在小于2小时内纠正测量剂量和预期剂量之间的任何差异 分钟这将使我们能够提供（质子）放射治疗在一个自适应的设置和大大减少的目标 每天的体积裕度（2 mm各向同性加2 mm质子治疗射束方向范围裕度）， 患者被定位在治疗台上。 我们建议通过同时开发快速的硬件和软件工具来实现这一目标， 室内即时伽马和锥形束CT成像结合快速剂量计算的优势。我们 将联合收割机结合这种技术与一种新的小波束适应框架。 虽然我们的一些方法也将改善光子治疗，但我们将专注于质子治疗，因为它 为体内剂量验证提供了独特的机会，也因范围不确定性而带来了独特的挑战。 我们的方法将提供给整个质子治疗社区。