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.

虽然适应性疗法之前已经被研究过，但之前的研究都没有涉及到独特的挑战 （范围不确定性）和质子的独特功能（子束优化和即时伽玛成像） 治疗。该提案将首次通过开发创新硬件和解决方案来解决这些问题 软件方法论。我们预计治疗计划和实施将完全适应内部情况 患者几何形状的微小变化。 该提案旨在预测患者在接受治疗之前的剂量分布（或其替代） 治疗实施并纠正测量剂量与预期剂量之间小于 2 的任何差异 分钟。这将使我们能够在适应性环境和大大减少的目标中提供（质子）放射治疗 每天进行体积裕度（质子治疗中在射束方向上 2 毫米各向同性加 2 毫米范围裕度），同时 患者位于治疗台上。 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 室内即时伽玛和锥束 CT 成像与快速剂量计算相结合的优势。我们 将把这项技术与小束适应的新颖框架结合起来。 虽然我们的一些方法也将改善光子疗法，但我们将重点关注质子疗法，因为它 为体内剂量验证提供了独特的机会，也提供了由于范围不确定性而带来的独特挑战。 我们的方法将提供给整个质子治疗界。