Medical Imaging of Peripheral Nerve Injury and Repair

周围神经损伤是四肢创伤性损伤的衰弱后果， 士兵，和脊髓损伤（SCI）后的次要结果。同样具有毁灭性的是， 和一般人群，是在切除周围组织、疾病或医源性损伤过程中的神经损伤。 神经损伤的功能恢复通常不完全，导致运动功能受损，感觉丧失， 和痛苦.慢性神经损伤的恢复尤其差，这可能是由于未修复的神经或 延迟或失败的主要修复。治疗神经损伤的一个主要挑战是目前缺乏有效的治疗方法。 神经医学成像的方法。因此，外科医生无法准确评估 神经损伤，以知情和数据驱动的方式计划手术干预，或评估 在早期时间点的神经修复，以预测长期恢复。 我们已经开发出创新的定量神经成像策略，克服了挑战 是由神经的复杂结构和构造造成的。我们的方法采用了两个广泛部署的 临床模式、超声（US）和磁共振成像（MRI）。我们的成像方法是 受强有力的初步数据的激励，这些数据表明定量US（qUS）和MRI（qMRI）是强大的 和临床上可行的策略，用于以高分辨率和高分辨率监测神经结构和组成， 灵敏度这些方法还旨在为生物变化提供非侵入性替代物， 发生在神经变性和再生过程中，但无法在患者中进行评估。建立 qUS和qMRI在临床环境中评估神经结构和组成的有效性和实用性，我们将 在大鼠（目标1）和人类（目标2）急性和慢性神经损伤模型中解决以下具体目标： 1)旨在测试定量US和MR成像策略在评估神经肌肉形态学方面的有效性 大鼠急性和慢性模型周围神经变性和修复后的组成 坐骨神经损伤2)测试定量US和MR成像策略在评估 人正中神经和尺神经急性和慢性模型的神经肌肉形态和组成 手术修复前即刻损伤。 我们的方法有可能改变神经损伤的诊断和治疗。立即临床 影响包括：（i）客观指导决定是否进行神经修复或神经转移手术; (ii)预测和监测神经修复的疗效;（iii）指导术后康复方案。这 这项工作还将为一项前瞻性研究奠定基础，在这项研究中，成像策略将被部署作为预后指标。 用于预测神经修复后神经肌肉恢复功效的工具，以改善治疗 退伍军人和普通人群的神经损伤。更广泛地说，我们的神经成像策略将有助于 诊断和预测更广泛的神经病变和神经疾病的神经结构和损伤。

Peripheral nerve damage is a debilitating consequence of both traumatic injury to the extremities of soldiers, and a secondary outcome following spinal cord injury (SCI). Equally devastating, for both the Veteran and general population, is nerve damage during excision of surrounding tissue, disease, or iatrogenic injury. Functional recovery from nerve damage is often incomplete, resulting in impaired motor function, sensory loss, and pain. Recovery is especially poor for chronic nerve injuries, which can result from unrepaired nerves or from delayed or failed primary repair. A major challenge in treating nerve injury is the current lack of effective methods to medically image nerves. As a consequence, surgeons are unable to accurately assess the extent of nerve injury, plan surgical intervention in an informed and data-driven manner, or assess the success of nerve repair at early time points, to predict long-term recovery. We have developed innovative quantitative strategies for nerve imaging, which overcome challenges created by the complex structure and architecture of nerves. Our approach employs two widely deployed clinical modalities, ultrasound (US) and magnetic resonance imaging (MRI). Our imaging approach is motivated by strong preliminary data demonstrating that quantitative US (qUS) and MRI (qMRI) are powerful and clinically feasible strategies for monitoring nerve structure and composition at high-resolution and with high sensitivity. These approaches are also intended to provide non-invasive surrogates for biological changes that occur during nerve degeneration and regeneration, but which cannot be evaluated in patients. To establish the validity and utility of qUS and qMRI in evaluating nerve structure and composition in a clinical setting, we will address the following specific aims in rat (Aim 1) and human (Aim 2) models of acute and chronic nerve injury: 1) To test the efficacy of quantitative US and MR imaging strategies in assessing neuromuscular morphology and composition during peripheral nerve degeneration and following repair of acute and chronic models of rat sciatic nerve injury. 2) To test the clinical efficacy of quantitative US and MR imaging strategies in assessing neuromuscular morphology and composition in acute and chronic models of human median and ulnar nerve injury immediately prior to surgical repair. Our approach has the potential to transform the diagnosis and treatment of nerve injury. Immediate clinical impacts include: (i) objective guidance on deciding whether to perform nerve repair or nerve transfer surgery; (ii) predicting and monitoring the efficacy of nerve repair; (iii) guidance for post-op rehabilitation protocols. This work will also set the stage for a prospective study in which imaging strategies will be deployed as prognostic tools for predicting the efficacy of neuromuscular recovery following nerve repair, towards improved treatment of nerve injury in Veteran and general populations. More broadly, our nerve imaging strategy will aid in the diagnosis and prediction of nerve structure and damage for a wider array of neuropathies and nerve diseases.