Multi-Task MR Simulation for Abdominal Radiation Treatment Planning

放射治疗计划（RTP）的准确性在很大程度上影响着外照射的有效性 放疗（EBRT）。个性化RTP从“模拟”开始，其中患者处于治疗位置 通常使用计算机断层扫描（CT）进行扫描，以确定治疗目标和危险器官（OAR）。 当软组织对比度不足以支持基于CT的RTP中的准确目标和OAR描绘时， 使用保守的大处理边界来避免几何遗漏。这种粗暴的治疗方法 在不超过周围正常组织的耐受性的情况下向肿瘤递送足够的辐射剂量。 磁共振（MR）可用作CT的补充模拟平台，以改善软组织 引人注目然而，这样一个复杂的，昂贵的和繁琐的多模式RTP工作流沿着不可避免的 系统性MR-CT配准误差限制了其在EBRT中的应用，特别是在腹部部位 由此解剖结构是高度移动的。在过去的几年里，人们对MR的集成产生了浓厚的兴趣。 单独进入RTP甚至治疗工作流程（即MR引导放射治疗，MRgRT）。腹部构成 MR模拟的关键挑战。目前的MR成像序列是次优的，以产生无运动 图像并解析呼吸运动。腹部RTP的MR数据处理尚不成熟。轮廓 目标和OAR的测量通常依赖于耗时且易于变化的手动、繁琐的过程。 在本提案中，我们将大幅改进MR采集和自动多器官分割， MR作为一种模拟模式的潜力可以充分释放，用于腹部EBRT。三个具体目标 将完成。在目标1中，我们将开发和验证独立的多任务MR（MT-MR）序列 专用于腹部MR模拟。在目标2中，我们将开发一个基于MT-MR仿真的多器官自动 分割工具在目标3中，我们将优化基于深度学习的剂量预测模型，并评估 基于MT-MR的RTP工作流程在自适应立体定向体部放射治疗计划中的有效性， 胰腺癌患者。该项目的成功完成将大大促进临床采用 腹部RTP的MR模拟，这将提高治疗精度和结局。而且 开发的技术将为未来的研究打开大门，旨在优化癌症诊断和 放疗

The accuracy of radiation treatment planning (RTP) heavily influences the effectiveness of external beam radiotherapy (EBRT). Individualized RTP begins with a “simulation”, in which the patient in a treatment position is commonly scanned using computed tomography (CT) to define the treatment target and organs at risk (OARs). When soft-tissue contrast is inadequate to support accurate target and OAR delineation in CT based RTP, conservatively large treatment margins are used to avoid a geometric miss. The crude treatment prevents delivering sufficient radiation dose to the tumor without exceeding the tolerance of surrounding normal tissues. Magnetic resonance (MR) can be used as a simulation platform complementary to CT for improved soft-tissue conspicuity. Yet, such a complicated, costly and tedious multi-modal RTP workflow along with unavoidable systematic MR-CT co-registration errors has limited its applications in EBRT, especially at the abdominal site whereby anatomies are highly mobile. Over the past few years, there is a keen interest in the integration of MR alone into RTP and even the therapy workflow (i.e. MR-guided radiotherapy, MRgRT). The abdomen poses critical challenges to MR simulation. Current MR imaging sequences are suboptimal to produce motion-free images and resolve respiratory motion. MR data processing for abdominal RTP is underdeveloped. Contouring of target and OARs typically relies on manual, tedious procedures that are time-consuming and variation-prone. In this proposal, we will substantially improve the MR acquisition and automated multi-organ segmentation, so the potential of MR as a simulation modality can be fully unleashed for abdominal EBRT. Three specific aims will be completed. In Aim 1, we will develop and validate a standalone multi-task MR (MT-MR) sequence dedicated to abdominal MR simulation. In Aim 2, we will develop an MT-MR simulation based multi-organ auto- segmentation tool. In Aim 3, we will optimize a deep learning-based dose prediction model and assess the effectiveness of the MT-MR based RTP workflow in adaptive stereotactic body radiotherapy planning of pancreatic cancer patients. Successful completion of the project will significantly promote the clinical adoption of MR simulation for abdominal RTP, which will improve treatment precision and outcomes. Moreover, the developed techniques will open the door to future studies aiming at optimizations in both cancer diagnosis and radiotherapy.