Virtual Functional Anatomy

虚拟功能解剖学

• 批准号: 7215882
• 负责人: frances t sheehan
• 金额: --
• 依托单位: CLINICAL CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7215882
• 关键词: anatomy; bioimaging /biomedical imaging; biomechanics; bone density; clinical research; computer simulation; digital imaging; human subject; joints; knee; magnetic resonance imaging; mathematical model; musculoskeletal disorder diagnosis; musculoskeletal imaging /visualization /scanning; noninvasive diagnosis; physiology; technology /technique development; three dimensional imaging /topography

The Virtual Functional Anatomy project is designed to fill the important knowledge gap that exists between the relationship of normal or impaired joint structure/function and the functional movement limitations associated with performing activities of daily living. Our current focus is to develop and ultimately validate a combined set of tools that will enable the accurate and precise measurement, analysis and visualization of three-dimensional (3-D) static and dynamic musculoskeletal anatomy (i.e., bone shape, skeletal kinematics, tendon and ligament strain, muscle force, and joint space). We plan to combine MRI imaging and analysis capabilities with a highly accurate, imaging-based measurement and analysis technique for the non-invasive quantification of complete joint anatomy and tissue dynamics during functional movements. This will require the development of a method for creating 3D digital images of loaded and moving joint tissues (bone, cartilage, and connective tissues) to reveal joint contact patterns and tissue loads. We will also evaluate the variability of bone shape and the sensitivity of defined joint posture (translation and rotation of one bone relative to another) to osteo-based coordinate system definition. We intend to use these capabilities to document and evaluate the function of normal and impaired joint structures (e.g., ACL rupture and patellar tracking syndrome) under simulated conditions experienced during activities of daily living. Recently, this work has concentrated on two primary project areas: 1) VFA tool development and 2) In vivo normal and impaired knee joint function. VFA Tool Development Over the past year, we have maintained a research focus on developing the backbone for VFA and began to explore the issues surrounding the dynamic MR scanning of the musculoskeletal system. The key focal points for the algorithm development were the image registration process along with continuing improvement in the integration algorithms.. Fast-PC MRI can provide 3D kinematics information for the bones of a joint (e.g., knee and ankle) as the subject brings this joint through a specified range of motion. Yet, this information cannot be readily applied to 3D models of the bones, which are created from static high-resolution scans of the joint. In order to apply the kinematics from the fast-PC MRI to the static models, the two image data sets have to be aligned (e.g., registered). Visualization is made possible by programs that have been written in-house using Matlab?s scripting language. This registration process led to the first dynamic cartilage contact model, measured non-invasively and in vivo, being developed this year. In Vivo Normal and Impaired Knee Joint Function On the experimental side, a primary focus has been on evaluating the clinical applicability of the tools being developed by applying them to children and adults diagnosed with Cerebral Palsy (n=7) Ehlers Danlos syndrome (n=6), stroke (n=1) and patellofemoral pain syndrome (n=1). We are in the process of analyzing the data acquired in order to quantify the various musculoskeletal parameters, such as joint kinematics, tendon strains, and tendon moment arms. As we complete the VFA toolbox, we should also be able to quantify forces in the quadriceps muscles, patellar tendon, the anterior cruciate ligament, and the cartilage during an extension/flexion cycle of the knee joint. Since the forces in the muscles are being calculated by measuring the strain in the tendons, it is imperative that errors be minimized during this measurement. Thus, in the normative population we are maximizing the strain within the tendons by maximizing the load being raised in extension, through the use of non-magnetic ankle weights. We are currently conducting a study to test the maximum weight that can be used without disrupting the repeatability of the motion. Moving forward, there are three major focus areas for this project. The first is to quantify healthy knee joint dynamics during loaded tasks that mimic functional activities of daily living. These data will then be compared to the knee joint dynamics of impaired subjects. The second project is similar, in that we are quantifying the 3D kinematics of the bones of the ankle joint during loaded tasks mimicking functional activities of daily living. Lastly, we are continue to progress towards improvements in imaging time and data accuracy through algorithm and image sequence development.

虚拟功能解剖项目旨在填补正常或关节功能受损或与日常生活活动相关的功能运动限制之间存在的重要知识差距。我们目前的重点是开发并最终验证一组组合的工具，这些工具将使三维（3-D）静态和动态肌肉骨骼解剖结构（即骨形，骨骼形状，骨骼运动学，肌腱，肌腱和韧带，肌肉和肌肉力量以及关节空间）进行准确，精确的测量，分析和可视化。我们计划将MRI成像和分析功能与高度准确，基于成像的测量和分析技术相结合，以在功能运动过程中对完全关节解剖结构和组织动力学进行非侵入性定量。这将需要开发一种方法来创建3D数字图像的加载和移动关节组织（骨骼，软骨和结缔组织），以揭示关节接触模式和组织负载。我们还将评估骨形的变异性以及定义的关节姿势（一个骨相对于另一个骨相对于另一个骨骼的翻译和旋转）的灵敏度对基于骨的坐标系定义。我们打算使用这些功能来记录和评估正常和受损的关节结构（例如ACL破裂和pat骨跟踪综合征）的功能，这是在日常生活活动中所经历的模拟条件下。最近，这项工作集中在两个主要项目领域：1）VFA工具开发和2）体内正常和受损的膝关节功能。 VFA工具开发 在过去的一年中，我们一直在研究为VFA开发主链的研究，并开始探索围绕肌肉骨骼系统动态MR扫描的问题。算法开发的关键焦点是图像注册过程以及整合算法的持续改进。快速-PC MRI可以为关节的骨骼（例如膝盖和脚踝）提供3D运动学信息，因为该受试者通过指定的运动范围为此带来了关节。但是，这些信息不能容易地应用于骨骼的3D模型，这些模型是由关节的静态高分辨率扫描产生的。为了将Kinematics从Fast-PC MRI应用于静态模型，必须对两个图像数据集对齐（例如，注册）。使用Matlab的脚本语言在内部编写的程序使可视化成为可能。该注册过程导致了今年开发的第一个动态软骨接触模型，该模型是非侵入性和体内测量的。 体内正常和膝关节功能受损 在实验方面，主要重点是通过将其应用于诊断为脑瘫（n = 7）Ehlers Danlos综合征（n = 6），Stroke（n = 1）和patellofofemoral Pain综合征（n = 1）的儿童和成年人（n = 6），ehlers ehlers danlos综合征（n = 6）。我们正在分析获得的数据，以量化各种肌肉骨骼参数，例如关节运动学，肌腱菌株和肌腱矩臂。当我们完成VFA工具箱时，我们还应该能够量化股四头肌肌肉，tellar肌腱，前交叉韧带和膝关节延伸/屈曲周期中的软骨。由于通过测量肌腱中的应变来计算肌肉中的力，因此在此测量过程中必须最小化误差。因此，在规范种群中，我们通过使用非磁踝重量来最大化肌腱内的应变。我们目前正在进行一项研究，以测试可以使用的最大重量，而无需破坏运动的重复性。 展望未来，该项目有三个主要的重点领域。首先是在模仿日常生活的功能活动中量化健康的膝关节动力学。然后将这些数据与受试者受损的膝关节动力学进行比较。第二个项目是类似的，因为我们正在量化踝关节骨骼的3D运动学在模仿日常生活功能活动的任务中。最后，通过算法和图像序列开发，我们将继续朝着改进成像时间和数据准确性的改善。