Robust workflow software for MRI tracking of glymphatic-lymphatic coupling

总结 我们母基金的主要目标（R 01 AT 011419，“CNS液体稳态中的淋巴-甘氨酰化”） 由NCCIH支持，专注于了解健康（啮齿动物）中的胶质细胞-淋巴偶联 个脑袋胶质淋巴和淋巴系统是中枢神经系统（CNS）液体控制的关键 体内平衡和废物处理。我们目前正在研究如何生理演习，如变化， 身体姿势和/或深吸气呼吸影响这两个系统 维持健康的大脑然而，一个固有的问题，为及时发展的补充， 治疗学是追踪胶质淋巴细胞和淋巴细胞之间的功能相互作用的技术挑战。 淋巴系统，这导致了关于大脑浪费的方向性和驱动力的争议 处置这些争议被认为是由异质性实验方法引起的， 最重要的是由于缺乏用于处理动态磁共振的鲁棒计算框架 成像（MRI）光学成像体内数据。在我们的母基金中，我们通过以下方式应对这些挑战： 建立数据驱动的统一计算框架来描述胶质淋巴运输和脑清除 基于正则化最优质量传输（rOMT）理论。我们开发了一个计算源代码 处理从动态对比增强磁共振成像（DCE-MRI）获得的数据， 头部和颈部的水平。然而，很明显，几个附加的后处理 需要对数据进行降噪的步骤，特别是在DCE-MRI采集的颈部水平 由于各种物理降级因素而固有地有噪声。我们已经将原始的rOMT代码与 科学界，并提出了一个rOMT处理工具箱，以纳入源项， 将允许跟踪废物清除，而无需任何可能不成立的质量保持假设 真实世界的数据。然而，尽管我们共享了源代码，但只有拥有昂贵的MATLAB许可证的用户才能使用。 和编码经验可以运行它，需要更多的软件工程来开发一个强大而有用的 框架软件包的用户社区。这一行政补充的目标是：1） 实现并统一4D DCE-MRI图像的时间和空间去噪算法，以保留 引流流和解剖结构结合rOMT流跟踪，以及2）完善我们现有的 rOMT软件框架，并将其转换为用户友好的基于Python的包。目标1的重点是 开发用于对在颈部和颅骨采集的定量DCE-MRI数据进行降噪的计算方法 基地，尤其是。在目标2中，我们将把开发的4D去噪和rOMT流体跟踪管道转换为 云就绪格式，并将其集成到基于插件的图形用户界面（GUI）中，并测试其操作 效率和可用性。在Aim 3中，我们将专注于数据管理（用户手册，视频教程）和代码 对用户社区的可用性。

Summary The major goal of our parent grant (R01AT011419, “Lymphatics-Glymphatics in CNS Fluid Homeostasis”) supported by the NCCIH is focused on understanding glymphatic-lymphatic coupling in the healthy (rodent) brain. The glymphatic and lymphatic systems are pivotal for the control of central nervous system (CNS) fluid homeostasis and waste disposal. We are currently studying how physiological maneuvers such as changes in body posture and/or deep-inspiratory breathing affect the two systems and therefore be therapeutically beneficial for sustaining a healthy brain. However, an inherent problem for the timely development of complementary therapeutics is the technical challenge involved in tracking the functional interplay between the glymphatic and lymphatic systems, which have led to controversies regarding the directionality and driving forces of brain waste disposal. These controversies are thought to have arisen from heterogeneous experimental approaches, and most importantly from the lack of a robust computational framework for processing dynamic magnetic resonance imaging (MRI) optical imaging in vivo data. In our parent grant, we are addressing these challenges by establishing a data-driven, unified computational framework to describe glymphatic transport and brain clearance based on regularized optimal mass transport (rOMT) theory. We have developed a computational source code to process data derived from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) acquired at the level of the head as well as the neck. However, it has become evident that several additional post-processing steps are needed for denoising the data, in particular, at the level of the neck where the DCE-MRI acquisitions are inherently noisy due to various physical degrading factors. We have already shared the raw rOMT code with the science community and also advanced an rOMT processing toolbox to incorporate the source term which will allow for tracking of waste clearance without any assumptions about mass preservation which may not hold in real-world data. However, although we shared the source code, only users with expensive MATLAB licenses and coding experience can run it, and more software engineering is required to develop a robust and useful framework software package for the user community. The goal of this administrative supplement is to: 1) implement and unify algorithms for temporal and spatial denoising of 4D DCE-MRI images to preserve the draining streams and anatomical structures in conjunction with rOMT flow tracking, and 2) refine our existing rOMT software framework and convert it into a user-friendly Python based package. Aim 1 is focused on developing the computational approach for denoising quantitative DCE-MRI data acquired at the neck and skull base, in particular. In Aim 2, we will convert the developed 4D denoising and rOMT fluid tracking pipeline into a cloud-ready format and integrate it into a plug-in-based graphical user interface (GUI) and test its operational efficiency and usability. In Aim 3 we will focus on data management (user manuals, video tutorials) and code availability for the user community.