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 我们的父母赠款的主要目标（R01AT011419，“中枢神经系统液体稳态中的淋巴结淋巴结型”） 由NCCIH支持的侧重于了解健康（啮齿动物）中的糖淋巴耦合 脑。胶囊和淋巴系统是控制中枢神经系统（CNS）流体的关键 稳态和废物处理。我们目前正在研究身体操作，例如变化 身体姿势和/或深度呼吸会影响这两个系统，因此是有益的 维持健康的大脑。但是，及时开发完件的继承问题 治疗方法是跟踪Glycatic和 淋巴系统引起了有关大脑浪费的方向性和驱动力的争议 处理。这些争议被认为是由异质的实验方法引起的，并且 最重要的是，由于缺乏处理动态磁共振的强大计算框架 成像（MRI）体内数据的光学成像。在我们的父母赠款中，我们正在解决这些挑战 建立一个数据驱动的统一计算框架，以描述淋巴运输和大脑清除率 基于正则最佳质量运输（ROMT）理论。我们已经开发了计算源代码 处理从动态对比增强的磁共振成像（DCE-MRI）中得出的数据 头部和脖子的水平。但是，已经证明了几个其他后处理 需要在DCE-MRI获取的颈部级别上授予数据的步骤 由于各种物理降解因素，因此本质上是噪音。我们已经与原始ROMT代码共享 科学界，还提出了一个ROMT处理工具箱，以合并来源术语 将允许跟踪废物清除，而无需对可能无法持有的质量准备的任何假设 在实际数据中。但是，尽管我们共享了源代码，但只有拥有昂贵MATLAB许可证的用户 编码体验可以运行它，并且需要更多的软件工程来开发强大而有用的 用户社区的框架软件包。这种行政补充的目的是：1） 实施并统一算法，用于4D DCE-MRI图像的临时和空间denoising 排水流和解剖结构与Romt Flow Tracking结合使用，2）完善我们现有的 ROMT软件框架并将其转换为基于用户友好的python软件包。 AIM 1专注于 开发用于确定颈部和头骨中获取的定量DCE-MRI数据的计算方法 尤其是基础。在AIM 2中，我们将将开发的4D Denoising和Romt Fluid Trackeline Pupeline转换为 云准备格式并将其集成到基于插入的图形用户界面（GUI）中并测试其操作 效率和可用性。在AIM 3中，我们将重点介绍数据管理（用户手册，视频教程）和代码 用户社区的可用性。