Robust workflow software for MRI tracking of glymphatic-lymphatic coupling

用于 MRI 跟踪类淋巴耦合的强大工作流程软件

• 批准号: 10609195
• 负责人: Helene D Benveniste
• 金额: $ 24.22万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609195
• 关键词: Address; Administrative Supplement; Affect; Algorithms; Anatomy; Animals; Biological; Brain; Breathing; Cerebrospinal Fluid; Cervical lymph node group; Clinical Research; Code; Communities; Computer software; Coupling; Data; Data Files; Deep Cervical Lymph Node; Detection; Development; Diffusion; Drainage procedure; Exposure to; FAIR principles; Fluid Balance; Fluid Waste Disposal; Goals; Head; Image; Imaging Techniques; Language Tests; Licensing; Liquid substance; Lymphatic; MRI Scans; Magnetic Resonance Imaging; Manuals; Measurement; Methods; Motion; National Center for Complementary and Integrative Health; Neck; Neuraxis; Noise; Pathway interactions; Physiological; Placebos; Plug-in; Posture; Process; Programming Languages; Publications; Pythons; Rattus; Refuse Disposal; Research; Resolution; Rodent; Running; Sampling; Science; Signal Transduction; Software Engineering; Software Framework; Software Tools; Source; Source Code; Speed; Stream; System; Testing; Therapeutic; Time; Tissues; base; biomedical imaging; cerebrospinal fluid flow; cloud platform; computer framework; contrast enhanced; data format; data management; deep learning; denoising; driving force; experience; experimental study; fluid flow; glymphatic system; graphical user interface; image processing; in vivo optical imaging; interest; novel; open source; parent grant; pre-clinical; preservation; reconstruction; simulation; skull base; software development; solute; theories; treatment response; usability; user-friendly; vector; wasting

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.

概括 我们家长资助的主要目标（R01AT011419，“中枢神经系统液体稳态中的淋巴管-类淋巴管”） 由 NCCIH 支持的重点是了解健康（啮齿动物）的类淋巴耦合 大脑。类淋巴系统和淋巴系统对于控制中枢神经系统 (CNS) 液体至关重要。 我们目前正在研究生理策略如何变化。 身体姿势和/或深吸气呼吸会影响这两个系统，因此对治疗有益 然而，对于维持健康的大脑来说，补充的及时发展存在一个固有的问题。 治疗学是跟踪类淋巴系统和类淋巴系统之间的功能相互作用所涉及的技术挑战。 淋巴系统，这引发了关于脑废物的方向性和驱动力的争议 这些争议被认为是由异质实验方法引起的，并且 最重要的是缺乏用于处理动态磁共振的强大计算框架 在我们的母基金中，我们正在通过体内成像（MRI）光学成像来解决这些挑战。 建立数据驱动的统一计算框架来描述类淋巴运输和大脑清除 基于正则化最优质量传递（rOMT）理论，我们开发了计算源代码。 处理从动态对比增强磁共振成像 (DCE-MRI) 获取的数据 然而，很明显，一些额外的后处理。 需要采取步骤对数据进行去噪，特别是在 DCE-MRI 采集的颈部水平 由于各种物理降级因素，它们本身就有噪音，我们已经与他们共享了原始 rOMT 代码。 科学界还提出了一个 ROMT 处理工具箱来合并源术语 将允许跟踪废物清除，而无需任何关于大规模保存的假设，这可能不成立 然而，尽管我们共享了源代码，但仅限于拥有昂贵 MATLAB 许可证的用户 和编码经验可以运行它，并且需要更多的软件工程来开发一个健壮且有用的 该管理补充的目标是： 1) 实施并统一 4D DCE-MRI 图像的时间和空间去噪算法，以保留 结合 rOMT 流量跟踪排出流和解剖结构，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。 Aim 1 的重点是。 开发对颈部和颅骨采集的定量 DCE-MRI 数据进行去噪的计算方法 特别是在目标 2 中，我们将把开发的 4D 去噪和 rOMT 流体跟踪管道转换为一个。 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 在目标 3 中，我们将重点关注数据管理（用户手册、视频教程）和代码。 用户社区的可用性。