Enabling Kinematic Joint Profiling Using MRI

项目摘要 我们提出了一个技术可行性研究，寻求发展的方法，定量运动学profiling的移动 使用磁共振成像（MRI）检查关节。在本研究的背景下，运动学轮廓被定义为 在运动过程中计算和跟踪的关节特征的集合。这个项目的动机是 通过假设这种运动关节的轮廓可以突出功能障碍，治疗进展和点 有利（或不利）的手术干预。在高级别上，设想拟议的 运动学特征可以适用于临床管理工作，其方式与血液生物标志物面板大致相同。 虽然关节的运动学成像可以使用平片（PF）X射线、计算机断层扫描（CT）， 和超声（US）方法，MRI是先进骨科评估的金标准， 附件运动分析选项。一组相对快速的运动学剖面采集可能是可行的， 添加到常规骨科MRI检查中，从而在静态和运动学方面提供最佳诊断成像 在一次访问中。 虽然一些初步研究已经暗示了运动成像数据的潜在诊断价值， 这些数据很难解释，也不容易艾德或记录在临床记录中。本研究 寻求建立能够提供简单和易于理解的运动学成像报告的基本方法 使用常规临床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调查，手腕的运动成像将进行研究。 腕舟骨和月骨的功能障碍是一个被广泛研究的诊断性运动学问题 重要性。腕部的新型4D零回波时间MRI将用于捕获运动学成像， 在两种已建立的腕关节运动模式期间，对舟状骨-月骨力学进行分析。 本项目的目标是建立和证明MRI运动学所需的方法学组件 profiling.为此目的，建议对100名健康对照受试者的中等规模队列进行数据收集。 新的MRI脉冲序列和后处理开发组件被引入并负责分析 这些规范性数据。使用所采集的MRI数据，将计算每个动态数据集的运动学参数。 提取并整理成每个主题的多参数配置文件。 该研究的目的2提出了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，本研究的目标3旨在使用机器学习聚类方法来开发一种运动学 使用所获得的对照数据集进行标准化程序。这种正常化是一个重要的里程碑， 将运动学轮廓转化为临床，并将为未来的 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.