Non-invasive assessment of ligament healing in vivo

描述（由申请人提供）：本项目的总体目标是系统地完善和验证我们的新型磁共振成像方案，用于愈合软组织生物力学特性的无创测量。这种平移工具将有利于临床前和临床试验，以评估韧带和肌腱修复的新兴技术。在这项研究中，我们将重点放在前交叉韧带（ACL）作为韧带损伤的模型。我们已经表明，韧带尺寸的磁共振（MR）成像测量（即，组织的量）和信号强度（即，组织质量）与ACL的生物力学特性相关，并且韧带尺寸和信号强度的组合进一步改善了这些预测。不幸的是，信号强度取决于图像采集参数，因此取决于协议、磁体，因此取决于机构。然而，T2* 弛豫时间是与组织中的胶原组织相关的MR组织特性，其不依赖于采集。我们现在有初步的证据表明，T2* 松弛时间内愈合韧带提供了一个更可靠的估计的结构特性。下一步是完善和验证这种基于T2* MR的预测方法，用于韧带愈合的纵向研究。在目标1中，我们将获得两种修复策略后愈合ACL的体积和T2* 的MR测量值。将在横断面研究中的早期增殖、中期和后期重塑愈合阶段收集这些测量值，以检查模型在这些愈合阶段预测生物力学特性的稳健性，并在必要时将时间效应纳入模型。在目标2中，我们将对我们的基于MR的方法进行纵向研究，以验证其在区分两种已知会产生愈合差异的修复策略方面的实用性，并确定在愈合早期获得的扫描是否会预测长期的生物力学特性。这一发现将非常有价值，因为它将减少临床前和临床韧带愈合研究的周期时间。该项目将利用 我们建立的生物增强ACL修复的临床前模型。在本研究中，我们的基于MR的多元回归预测模型将在目标1中进行细化，并在目标2中进行验证。主要结局指标包括MR参数（T2* 弛豫时间、信号强度、体积）和愈合ACL的结构特性（失效载荷、线性刚度）。次要结局包括ACL材料特性（失效应力、切线模量）和膝关节松弛度。在研究完成时，我们将完善基于MR的预测模型，验证其在手术修复研究中的用途，并准备在ACL修复和重建的临床试验中使用MR技术作为主要结局指标。

DESCRIPTION (provided by applicant): The overall objective of this project is to systematically refine and validate our novel magnetic resonance imaging protocol for the non-invasive measurement of the biomechanical properties of healing soft tissues. This translational tool will be advantageous for pre-clinical and clinical trials to evaluate up-and-coming technologies for ligament and tendon repair. For this study we will focus on the anterior cruciate ligament (ACL) as a model of ligament injury. We have shown that magnetic resonance (MR) imaging measurements of ligament size (i.e., amount of tissue) and signal intensity (i.e., quality of tissue) correlate to the biomechanical properties of the ACL, and that the combination of ligament size and signal intensity further improved these predictions. Unfortunately, signal intensity is dependent on image acquisition parameters thus protocol, magnet, and hence institution dependent. However, T2* relaxation time is an MR tissue property that correlates to collagen organization in tissues that is not as acquisition dependent. We now have preliminary evidence to show that T2* relaxation times within a healing ligament provides a more reliable estimate of the structural properties. The next step is to refine and validate this T2* MR-based prediction method for use in longitudinal studies of ligament healing. In Aim 1, we will obtain MR-measures of volume and T2* of the healing ACL following two repair strategies. These measures will be collected in the early proliferative, middle and later remodeling stages of healing in a cross sectional study to examine how robustly the model predicts the biomechanical properties across these stages of healing, and to incorporate time effects into the model if necessary. In Aim 2, we will perform a longitudinal study of our MR-based approach to validate its utility for discriminating between two repair strategies known to produce differences in healing and to determine if scans obtained in the early stages of healing will predict the longer-term biomechanical properties. This finding would be very valuable as it would reduce the cycle time of both pre-clinical and clinical ligament healing studies. The project will take advantage of our well established pre-clinical model of bio-enhanced ACL repair. In this study, our MR based multi-regression prediction model will be refined in Aim 1 and validated in Aim 2. Primary outcome measures include the MR parameters (T2* relaxation time, signal intensity, volume) and the structural properties (failure load, linear stiffness) of the healing ACL. Secondary outcomes include the ACL material properties (failure stress, tangent modulus) and knee laxity. At study completion we will have refined our MR- based prediction models, validated its use for surgical repair studies, and be poised to use the MR technique as a primary outcome measure in clinical trials of ACL repair and reconstruction.