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.

虽然以前已经研究了自适应疗法，但以前的研究都没有涉及独特的挑战 （范围不确定性）和质子的独特功能（Beamlet优化和及时伽马成像） 治疗。该建议将首次通过开发创新的硬件和 软件方法。我们设想治疗计划和交付将完全自适应 患者几何形状的分数变化。 该建议旨在预测患者的剂量分布（或其替代物） 治疗交付并正确纠正测量剂量和预期剂量之间的任何差异 分钟。这将使我们能够在自适应环境中提供（质子）放射治疗 每天的体积边缘（2mm的各向同性加质子治疗的2mm范围2mm范围） 患者位于治疗桌上。 我们建议通过同时开发快速硬件和软件工具来实现这一目标 室内提示伽玛和锥束CT成像的优势与快速剂量计算结合使用。我们 将把这项技术与Beamlet适应的新框架相结合。 虽然我们的某些方法也可以改善光子疗法，但我们将专注于质子治疗 提供了在体内剂量验证的独特机会，以及由于范围不确定性而引起的独特挑战。 我们的方法将提供给整个质子疗法社区。