Machine learning accelerated on-line adaptive replanning

抽象的。该方案的总体目标是开发和测试一种新型的机器学习(ML)加速 磁共振成像(MRI)引导辐射的在线自适应再规划(MOLAR)解 治疗(RT)(MRGRT)。在多分割RT过程中，肿瘤的位置、形状和大小以及 正常器官在不同组分之间差异很大。这些组间差异是主要的 限制RT靶向准确性的因素。目前图像引导RT(IGRT)的标准实践， 为解决基于锥束CT(CBCT)的碎片间差异而开发的，只能校正 翻译错误，因此不能完全解释分数间的变化。为了解决这个问题， 研究人员最近引入了在线自适应重新规划(OLAR)，它基于 当天的解剖，并交付了分数的计划。目前，有两个主要障碍影响着 OLAR：(1)CBCT无法准确描绘当天的解剖情况，以及(2)所需时间 执行OLAR的时间足够长，从而使其不切实际。提高描绘精度的一种方法是使用 核磁共振和CT。目前正在将MRI引导的OLAR引入临床，以显著改善RT 瞄准目标。然而，瓶颈仍然是分割解剖所需的不切实际的时间长度 一天，可以超过30分钟。此外，可用的合成CT(SCT)生成方法速度慢或 MRI引导下的OLAR不准确。目前还没有一种方法可以快速、客观地确定OLAR 是必要的。为了解决这些问题，我们计划在磨牙溶液中开发新的技术。我们 假设基于MRI的摩尔溶液将完全考虑组分间的变化，从而 大大提高了放射治疗期间的肿瘤靶向性和放射治疗的有效性。具体来说，我们的目标是 (1)开发实用的基于ML的解决方案，以快速确定OLAR的必要性并快速生成 精确的合成CT；(2)开发基于ML的技术，以显著加速OLAR的分割 使用循序渐进的三步法；和(3)通过以下方法验证磨牙的临床实用性和有效性 回顾和前瞻性地将磨牙应用于MRI设备上，以测试其在 考虑了组间差异。我们将开发这种新型的摩尔溶液，通过锻造独特的 临床物理学家、放射肿瘤学家和行业开发人员通过建立 学术与产业的伙伴关系。该项目的成功完成将使临床医生能够常规地 实行“影像-计划-治疗”，这是MRGRT的最佳解决方案。这一新的范式将充分说明 组间变异，提高肿瘤靶向性，降低正常组织毒性，最终鼓励 临床医生修改当前剂量和/或剂量分次，以增加治疗收益，增强患者 提高生活质量，和/或大幅节省医疗成本。我们提出的战略代表着一种极端的 背离了当前的做法。我们坚信，这一战略是RT交付的未来。

Abstract. The overall goal of this proposal is to develop and test a novel machine learning (ML) accelerated On-Line Adaptive Replanning (MOLAR) solution for magnetic resonance imaging (MRI) guided radiation therapy (RT) (MRgRT). During the multi-fraction RT process, the location, shape and size of tumors and normal organs vary significantly between the fractions. These interfraction variations are among the major factors that can limit the accuracy of RT targeting. The current standard practice of image-guided RT (IGRT), developed to address the interfraction variations based on cone-beam CT (CBCT), can only correct for translational errors, and thus does not fully account for interfraction changes. To address this issue, researchers recently introduced online adaptive replanning (OLAR) that generates a new plan based on the anatomy of the day and delivers the plan for the fraction. Currently, two main obstacles affect the success of OLAR: (1) the anatomy of the day cannot be delineated accurately based on CBCT, and (2) the time required to perform OLAR is long enough to render it impractical. One way to improve the delineation accuracy is to use MRI versus CT. MRI-guided OLAR is currently being introduced into the clinics to substantially improve RT targeting. However, the bottleneck is still the impractical length of time required to segment the anatomy of the day, which can exceed 30 minutes. Furthermore, available synthetic CT (sCT) generation methods are slow or inaccurate for MRI-guided OLAR. There is no method available to quickly and objective determine when OLAR is necessary. To address these issues, we plan to develop novel techniques in the MOLAR solution. We hypothesize that the MRI-based MOLAR solution will fully account for interfraction changes, thereby substantially improving tumor targeting during RT delivery and the effectiveness of RT. Specifically, we aim to (1) develop practical ML-based solutions to quickly determine the necessity of OLAR and to rapidly generate accurate synthetic CTs; (2) develop ML-based techniques to substantially accelerate segmentation for OLAR using a progressive three-step process; and (3) verify clinical practicality and effectiveness of MOLAR by retrospectively and prospectively applying the MOLAR on MRI sets to test its speed and effectiveness in accounting for interfraction variations. We will develop this novel MOLAR solution by forging unique collaborations between clinical physicists, radiation oncologists and industry developers via an established academic-industry partnership. The successful completion of this project will enable clinicians to routinely practice “image-plan-treat”, which is the optimal solution for MRgRT. This new paradigm will fully account for interfraction variations, improve tumor targeting, reduce normal tissue toxicity, and ultimately encourage clinicians to revise the current doses and/or dose fractionations to increase therapeutic gain, enhance patient quality of life, and/or substantially save on healthcare costs. Our proposed strategy represents a drastic departure from current practice. We firmly believe that this strategy is the future of RT delivery.