Enabling Kinematic Joint Profiling Using MRI

项目摘要 我们提出了一项技术可行性研究，寻求开发运动的定量运动学规划方法。 使用核磁共振成像(MRI)进行关节检查。在本研究的背景下，运动学Profile被定义为fiNed 在移动过程中计算和跟踪的关节特征的集合。这个项目是有动力的 通过假设这种活动关节的fi可以突出功能障碍、治疗进展和要点 倾向于有利的(或不利的)外科干预。在高级别上，预计拟议的 运动学专业fiLES可以以与血液生物标志物面板非常相似的方式fit t toflwws临床管理工作。 虽然关节的运动成像可以使用Plain-film(PF)X射线，计算机断层扫描(CT)， 和超声(US)方法，MRI是高级骨科评估的黄金标准，是一种有吸引力的 用于零件运动分析的选项。一系列相对快速的运动学专业fiLing收购可能是可行的 添加到常规的骨科MRI检查中，从而提供静态和动态的最佳诊断成像 一次访问中的上下文。 尽管几项初步研究已经暗示了运动成像数据的潜在诊断价值， 这样的数据很难解释，而且不容易在临床记录中被量化或捕获。在这项研究中，我们 寻求建立能够提供简单且易于理解的运动成像报告的基本方法 使用常规的临床MRI设备在短扫描间隔内获取数据。 作为这些方法的初步可行性研究，手腕的运动成像将被研究。 腕关节舟状骨和月骨功能障碍是一个广泛研究的诊断运动学问题。 SignifiCance.新的手腕4D零回声时间磁共振成像将被用来捕捉运动成像 在两种已建立的腕关节运动模式中，腕舟骨-月骨力学的ProfiLing。 本项目的目标是建立和演示MRI运动学所需的方法学组件 ProfiLing。为此目的，建议对100名健康对照受试者进行中等规模的队列数据收集。 介绍了一种新的磁共振脉冲序列及其后处理开发组件，并进行了分析 这一标准数据。使用所获取的MRI数据，每个动态数据集的运动学参数将 提取并整理成每个受试者的多参数PROfiLE。 研究目标2提出使用外部传感器运动捕捉方法来验证基于MRI的 对50%的研究队列进行运动学参数测量。 最后，研究的目标3试图使用机器学习聚类方法来开发运动学 使用所获取的对照数据集的ProfiLE归一化过程。这样的正常化是 将运动学fi翻译到临床，将为未来的翻译研究奠定基础 有症状的队列。

Project Summary We propose a technical feasibility study seeking to develop methods for quantitative kinematic profiling of moving joints using magnetic resonance imaging (MRI). In the context of this study, a kinematic profile is defined as a collection of joint characteristics computed and tracked during the course of movement. This project is motivated by the hypothesis that such profiling of moving joints can highlight dysfunction, treatment progress, and point towards favorable (or unfavorable) surgical interventions. At a high level, it is envisioned that the proposed kinematic profiles could fit into clinical management workflows much in the same way as blood biomarker panels. While kinematic imaging of joints can be performed using plain-film (PF) X-ray, computed tomography (CT), and ultrasound (US) methods, MRI is the gold-standard for advanced orthopedic assessment and is an appealing option for accessory kinematic analysis. A set of relatively fast kinematic profiling acquisitions could feasibly be added to routine orthopedic MRI exams, thereby providing optimal diagnostic imaging in both static and kinematic contexts within a single visit. Though several preliminary studies have hinted at the potential diagnostic value of kinematic imaging data, such data is difficult to interpret and cannot easily be quantified or captured in clinical records. In this study, we seek to establish fundamental methods that can provide simple and easily digestible kinematic imaging reports with data acquired in a short scan interval using conventional clinical MRI equipment. As a preliminary feasibility investigation of these methods, kinematic imaging of the wrist will be studied. Dysfunction of the scaphoid and lunate bones in the wrist is a well-studied kinematic problem of diagnostic significance. Novel 4D zero-echo-time MRI of the wrist will be used to capture the kinematic imaging using for profiling of the scaphoid-lunate mechanics during two established wrist movement patterns. The goal of this project is to establish and demonstrate methodological components required for MRI kinematic profiling. Data collection on a modest-sized cohort of 100 healthy control subjects is proposed for this purpose. Novel MRI pulse-sequence and post-processing development components are introduced and tasked for analysis of this normative data. Using the acquired MRI data, kinematic parameters for each dynamic dataset will be extracted and curated into a multi-parametric profile for each subject. Aim 2 of the study proposes the use of external sensor motion capture methods to validate the MRI-based kinematic parameter measurements on 50% of the study cohort. Finally, Aim 3 of the study seeks to use machine-learning clustering approaches to develop a kinematic profile normalization procedure using the acquired control dataset. Such normalization is a crucial milestone in the translation of kinematic profiling to the clinic and will establish a baseline for future translational studies of symptomatic cohorts.