Robust IMPT with automated beam orientation and scanning spot optimization

具有自动光束定向和扫描点优化功能的稳健 IMPT

• 批准号: 10112842
• 负责人: Ke Sheng
• 金额: $ 36.42万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10112842
• 关键词: Adoption; Algorithms; Clinic; Clinical; Complex; Data; Distal; Dose; Equilibrium; Face; Feedback; Goals; Individual; Intensity modulated proton therapy; Manuals; Maps; Methods; Modality; Modeling; Normal tissue morphology; Patients; Physics; Positioning Attribute; Protons; Research Personnel; Roentgen Rays; Scanning; Scheme; Spottings; System; Techniques; Testing; Time; Treatment Efficacy; Uncertainty; Validation; Variant; X-Ray Therapy; base; cancer therapy; clinical translation; combinatorial; cost; dosimetry; high dimensionality; improved; innovation; novel; proton beam; proton therapy; treatment optimization; treatment planning

Proton beams have emerged as an appealing new modality for cancer therapy. With continuing clinical adoption and technical advances in the past two decades, intensity modulated proton therapy (IMPT) using scanning pencil beams has been established as the desired delivery method to fully take advantage of proton physics. Thus far, IMPT optimization has mainly focused on modulating the scanning spots with manually selected beam angles. At the same time, for intensity modulated X-ray therapy (IMXT), researchers including the PI's group have demonstrated that superior dosimetry can be attained with integrated beam orientation optimization (BOO). Nevertheless, the benefit of BOO has not extended to IMPT due to the paramount computational challenges of solving the integrated BOO and scanning spot optimization (SSO) problem, which by itself is a higher-dimensional problem than the fluence map optimization problem in IMXT. Currently, IMPT BOO is considered a combinatorial problem that is not mathematically tractable with increasing problem size. Despite the computational challenge, compared with IMXT, BOO is more important for IMPT for the following reasons. First, the optimal number and orientations of beams for IMPT have not been known. While the BOO problem in X-ray therapy is often circumvented in practice by using single or multiple arc beams, the same technique applied to IMPT would increase the volumes of normal tissue being irradiated by the entrance dose and would therefore start losing its low dose sparing advantage. Furthermore, because IMPT beam time is restrictive, using many beams in a treatment fraction is operationally impractical. Subsequently, IMPT plan quality is heavily influenced by each of the few selected beams. Yet, manual IMPT beam orientation selection in the available non-coplanar solution space is unintuitive and ineffective. Second, IMPT plans are highly degenerate with different combinations of beams, spots and spot sparsity resulting in similar dosimetry but vastly different robustness to uncertainties. Existing worst-case optimization methods are a suboptimal compromise between the dosimetry and robustness. It is hypothesized that both the dosimetry and robustness will be significantly improved by integrating BOO in IMPT optimization. It is then hypothesized that the integrated optimization problem can be formulated as a group sparsity optimization problem with efficient solutions. To test these hypotheses, the following aims are proposed. Aim 1. Develop automated beam orientation and sparse spot optimization for IMPT. Aim 2. Develop fraction-variant IMPT. Aim 3. Incorporate sensitivity regularization (SenR) for robust beam orientation and scanning spot optimization. Aim 4. Validation of the integrated BOO, SSO and robustness optimization framework. The first three aims will be mainly performed at UCLA with the clinical and physics input from UPENN. The last aim will be mainly performed at UPENN. Depending on the feedback, UCLA will provide technical support to use and validate the proposed treatment planning system.

质子束已成为一种有吸引力的癌症治疗新方式。随着临床的持续 过去二十年的采用和技术进步，调强质子治疗（IMPT）使用 扫描笔形束已被确定为充分利用质子的理想传输方法 物理。到目前为止，IMPT 优化主要集中在手动调节扫描点上。 选定的光束角度。与此同时，对于调强 X 射线治疗 (IMXT)，研究人员包括 PI 小组已经证明，通过集成射束定向可以实现卓越的剂量测定 优化（BOO）。然而，由于最重要的原因，BOO 的好处并未扩展到 IMPT。 解决集成 BOO 和扫描点优化 (SSO) 问题的计算挑战， 它本身是一个比 IMXT 中的注量图优化问题更高维的问题。目前，IMPT BOO 被认为是一个组合问题，随着问题规模的增加，在数学上难以处理。 尽管存在计算挑战，但与 IMXT 相比，BOO 对于以下方面的 IMPT 更为重要： 原因。首先，IMPT 的最佳光束数量和方向尚不清楚。虽然嘘声 在实践中，X 射线治疗中的问题通常可以通过使用单个或多个弧束来解决，同样的 应用于 IMPT 的技术将增加入口剂量照射的正常组织的体积 因此将开始失去其低剂量节省优势。此外，由于 IMPT 波束时间为 由于限制性的，在一个治疗部分中使用许多光束在操作上是不切实际的。随后，IMPT计划 质量很大程度上受到少数选定光束的影响。然而，手动 IMPT 光束方向选择 在可用的非共面解决方案空间中是不直观且无效的。其次，IMPT计划高度 光束、光斑和光斑稀疏度的不同组合会退化，导致相似的剂量测定，但 对不确定性的鲁棒性差异很大。现有的最坏情况优化方法不是最理想的 剂量测定和鲁棒性之间的折衷。假设剂量测定和鲁棒性 通过将 BOO 集成到 IMPT 优化中，将得到显着改善。然后假设 集成优化问题可以表示为一个高效的群稀疏优化问题 解决方案。为了检验这些假设，提出了以下目标。目标1.开发自动化光束 IMPT 的方向和稀疏点优化。目标 2. 开发分数变体 IMPT。目标 3. 合并 灵敏度正则化 (SenR)，用于稳健的光束定向和扫描点优化。目标 4. 验证 集成的 BOO、SSO 和鲁棒性优化框架。前三个目标主要是 在 UCLA 进行，并得到 UPENN 的临床和物理输入。最后一个目标将主要执行于 宾夕法尼亚大学。根据反馈，加州大学洛杉矶分校将提供技术支持以使用和验证拟议的 治疗计划系统。