Development of A High Throughput Image-Guided IMRT System for Preclinical Research

项目概要/摘要 小动物临床前放射生物学实验是在人类使用前检验安全性和有效性的关键 临床试验然而，受限于目前可用的技术，临床前动物研究实质上不同 从最先进的人类治疗剂量一致性。因此，动物研究很难模拟 放射生物学、放射免疫学和人类治疗的毒性环境。这种差异对 我们有意义地测试人类翻译假设的能力。拥有数十年 随着放射治疗技术的进步，人类放射治疗已经实现了高靶向精度和剂量适形性， 技术突破，包括强度调制放射治疗（IMRT），这是不可用的小鼠 实验一个实用的设备和算法来调制的x射线强度的规模小动物是 弥合差距的第一步。在NIH R21基金的支持下，我们设计了一种新的小动物调强放射治疗， 剂量调制器称为稀疏正交准直器（SOC）。与硬件同样重要的是，我们创建了 使数学工具能够以比理论上更高的可实现分辨率提供SOC IMRT计划。 小型化MLC IMRT。我们对原型SOC进行了调试和测试， 计算机模拟和体模给药。尽管如此，在强度调制装置和非线性光学装置之间仍然存在大的差距。 以及适合广泛采用和影响的小动物IMRT系统。所需的时间、资源和 创建复杂的SOC-IMRT计划的培训与临床前设置不兼容。而且没有 由于自动化，现有的图像引导的小动物IMRT治疗对于治疗活体动物来说慢得令人望而却步 在麻醉状态下最后，在成像和治疗模式之间切换的当前手动方法导致 剂量输送中难以克服的不确定性。我们建议使用自动化、机器人和系统来填补这些空白。 优化.我们提出以下具体目标。具体目标1（SA1）。自动器官分割， 使用深度学习神经网络的小鼠。具体目标2（SA2）。开发一个功能齐全，自动化， 高效的IMRT系统。具体目标3（SA3）。机器人多鼠标自动化的开发和验证 用于自动成像和治疗的治疗环境（Multi-MATE）。除了剂量测定，我们还将量化 时间性能是小动物调强放射治疗系统的关键。因此，除了改善 硬件的准确性和可靠性，拟议的项目将提供一个完全自动化的规划和交付 系统，从而消除了广泛采用小动物调强放射治疗的最后障碍。的成功 拟议的项目将有助于现有的研究，以实现人类翻译的全部潜力，并使未来 假设检验，其中精确的复杂剂量分布至关重要。

Project Summary/Abstract Preclinical radiobiology experiments on small animals are crucial to test the safety and efficacy before human clinical trials. However, limited by currently available technologies, preclinical animal studies substantially differ from state-of-the-art human treatments in dose conformity. Consequently, the animal studies poorly mimic the radiobiological, radioimmunological, and toxicity environment of human therapies. The disparity adversely affects our ability to meaningfully test hypotheses that are intended for human translation. With decades of advancement, human radiotherapy has achieved high targeting accuracy and dose conformality based on technological breakthroughs, including intensity-modulated radiotherapy (IMRT), which is unavailable for mouse experiments. A practical device and algorithm to modulate the x-ray intensity for the scale of small animals is the first step to bridge the gap. With the support of an NIH R21 grant, we engineered a novel small animal IMRT dose modulator termed sparse orthogonal collimator (SOC). Equally important as the hardware, we created the enabling mathematical tools to deliver SOC IMRT plans with higher achievable resolution than a theoretically miniaturized MLC-based IMRT. We commissioned and tested prototypical SOCs to deliver highly modulated doses in silico and on phantoms. Nonetheless, there are still large gaps between an intensity modulation device and a small animal IMRT system suitable for broad adoption and impact. The required time, resources, and training to create sophisticated SOC-IMRT plans are incompatible with preclinical settings. Furthermore, without automation, the existing image-guided small animal IMRT treatment is prohibitively slow for treating live animals under anesthesia. Lastly, the current manual method to switch between imaging and therapy modes results in intractable uncertainties in dose delivery. We propose to fill these gaps using automation, robotics, and system optimization. We propose the following specific aims. Specific Aim 1 (SA1). Automated organ segmentation for mice using deep learning neural networks. Specific Aim 2 (SA2). Development of a fully functional, automated, and efficient IMRT system. Specific Aim 3 (SA3). Development and validation of a robotic Multi Mouse Automated Treatment Environment (Multi-MATE) for automated imaging and treatment. Besides dosimetry, we will quantify the time performance, which is critical to small animal IMRT system. As a result, in addition to improving the hardware accuracy and reliability, the proposed project will provide a fully automated planning and delivery system, thus removing the last barriers towards the broad adoption of small animal IMRT. The success of the proposed project will help existing research to achieve the full potential for human translation and enable future hypotheses testing where accurate complex dose distribution is critical.