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: 10379929
• 负责人: HARALD PAGANETTI
• 金额: $ 29.81万
• 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/10379929
• 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这种独特的挑战