Robust IMPT with automated beam orientation and scanning spot optimization

质子束已经成为一种吸引人的癌症治疗新方法。与持续的临床 在过去的二十年里，强度调制质子疗法(IMPT)的采用和技术进步 扫描铅笔束已被确立为充分利用质子的理想传输方式 物理学。到目前为止，IMPT优化主要集中在手动调制扫描光点 选定的光束角度。与此同时，对于强度调制X射线疗法(IMXT)，研究人员包括 PI的小组已经证明，使用集成的射束定向可以获得更好的剂量学 优化(Boo)。然而，由于至高无上的原因，BOO的好处并没有延伸到IMPT 求解集成BOO和扫描点优化(SSO)问题的计算挑战，该问题 其本身是一个比IMXT中的通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，该问题在数学上是不容易处理的。 尽管存在计算上的挑战，但与IMXT相比，BOO在以下方面对IMPT更重要 理由。首先，IMPT的最佳梁数和方向尚不清楚。当嘘声响起 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光束来绕过，相同的 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约优势。此外，由于入射光束时间是 限制性的是，在一个处理部分中使用许多束流在操作上是不切实际的。随后，IMPT计划 质量受所选的少数几个梁的影响很大。然而，手动入射光束方向选择 在现有的非共面解空间中，非共面解空间是不直观和低效的。其次，紧迫的计划非常重要。 由于光束、光斑和光斑稀疏性的不同组合而导致退化，导致相似的剂量测定，但 对不确定性的稳健性截然不同。现有的最坏情况优化方法是次优的 在剂量测定和稳健性之间进行折衷。假设剂量学和稳健性 通过将BOO集成到IMPT优化中，将显著提高性能。然后，假设 集成优化问题可以表示为一个有效的群体稀疏优化问题 解决办法。为了检验这些假设，我们提出了以下目标。目标1.开发自动波束 IMPT的定向和稀疏点优化。目的2.开发变分数IMPT。目标3.合并 敏感度正则化(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和健壮性优化框架。前三个目标将主要是 在加州大学洛杉矶分校进行，来自宾夕法尼亚大学的临床和物理输入。最后一个目标将主要在以下方面实现 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，以使用和验证建议的 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.