Robust IMPT with automated beam orientation and scanning spot optimization

质子束已经成为一种有吸引力的癌症治疗新模式。持续临床 采用和技术进步，在过去的二十年中，强度调制质子治疗（IMPT）使用 扫描笔形束已经被确立为充分利用质子的理想的递送方法。 物理学到目前为止，IMPT优化主要集中在手动调制扫描点， 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，使用集成射束定向可以获得上级剂量测定 优化（BOO）。然而，由于最重要的是， 解决集成BOO和扫描点优化（SSO）问题的计算挑战， 其本身是一个比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，它在数学上是不容易处理的。 尽管存在计算挑战，但与IMXT相比，BOO对于IMPT更重要，原因如下 原因首先，IMPT的波束的最佳数量和方向还不知道。当BOO X射线治疗中的问题在实践中通常通过使用单个或多个弧形射束来规避， 应用于IMPT的技术将增加由入射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射束时间是 限制性的，在治疗部分中使用许多射束在操作上是不切实际的。随后，IMPT计划 质量严重地受到所选择的几个波束中的每一个的影响。然而，手动IMPT波束方向选择 在可用的非共面解决方案空间是不直观和无效的。其次，IMPT计划高度 随着射束、光斑和光斑稀疏度的不同组合而退化，导致类似的剂量测定， 对不确定性的鲁棒性有很大不同。现有的最坏情况优化方法是一种次优方法， 剂量测定和耐用性之间的折衷。假设剂量测定和耐用性 通过在IMPT优化中集成BOO，将得到显著改善。然后假设， 集成优化问题可以表示为一个群稀疏优化问题， 解决方案为了验证这些假设，提出了以下目标。目标1.开发自动化光束 IMPT的定位和稀疏点优化。目标2.开发分数变体IMPT。目标3：纳入 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，临床和物理输入来自UPENN。最后一个目标将主要在 宾夕法尼亚大学。根据反馈，UCLA将提供技术支持，以使用和验证拟议的 治疗计划系统

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.