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

项目摘要/摘要 在小动物身上进行临床前放射生物学实验是在人类面前测试安全性和有效性的关键 临床试验。然而，受当前可用技术的限制，临床前动物研究有很大的不同 来自最先进的人类治疗方法，符合剂量标准。因此，动物研究很难模仿 人类治疗的放射生物学、放射免疫学和毒性环境。这种不平等会对 我们有能力有意义地测试用于人工翻译的假设。用几十年的时间 人类放射治疗已经取得了很高的靶向精度和剂量一致性 技术突破，包括调强放射治疗(IMRT)，这是小鼠无法获得的 实验。一种用于小动物尺度的X射线强度调制的实用装置和算法是 弥合差距的第一步。在NIH R21基金的支持下，我们设计了一种新型的小型动物IMRT 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 基于MLC的小型化调强放射治疗。我们委托并测试了原型SOC，以提供高度调制的 硅胶和幻影中的剂量。尽管如此，在强度调制装置之间仍有很大的间隙 以及适合广泛采用和影响的小动物调强放射治疗系统。所需的时间、资源和 制定复杂的SOC-IMRT计划的培训与临床前环境不相容。此外，如果没有 自动化，现有的图像引导的小动物调强放射治疗对活体动物的治疗速度非常慢。 在麻醉状态下。最后，当前在成像和治疗模式之间切换的手动方法导致 剂量传递中的难以处理的不确定性。我们建议使用自动化、机器人和系统来填补这些空白 优化。我们提出了以下具体目标。特定目标1(SA1)。自动器官分割 使用深度学习神经网络的小鼠。特定目标2(SA2)。开发一种功能齐全、自动化、 和高效的调强放射治疗系统。具体目标3(SA3)。机器人多鼠自动控制系统的研制与验证 用于自动成像和治疗的治疗环境(多配对)。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，建议的项目将提供完全自动化的计划和交付 系统，从而消除了小动物调强放射治疗广泛采用的最后障碍。的成功之路 拟议的项目将帮助现有研究充分发挥人工翻译的潜力，并使未来 假设测试，其中准确的复杂剂量分布是关键。

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.