Multi-Center Implementation and Validation of Efficient Magnetic Resonance Imaging and Analysis of Atherosclerotic Disease of the Cervical Carotid

摘要： 在过去的几十年里，大量的研究已经在磁共振方法上产生了实质性的创新 颅外颈动脉粥样硬化的特征。在理想下用这些创新获得的图像 这种情况给临床医生提供了关于动脉壁疾病的丰富信息和工具的希望。 对这一潜伏疾病的适当管理是极其必要的。尽管如此，互联网的巨大潜力 这项技术还没有进入常规的临床医疗机构。事实上，因为需要 格拉基造影剂(GBCA)，检查时间长(通常约45分钟，以获得 5或6个必要序列中的多个对比)，以及解释多个序列所需的陡峭学习曲线 对比剂MRI大多数从业者在评估风险时仍采用简化的直径狭窄指标。之后 经过多年的集体调查，参与这项提案的调查人员联盟认为 现在是解决这些剩余限制并最终改变临床范式的时候了。 总体假设：为了实现颈动脉疾病治疗的巨大潜力， 需要高效、易用的磁共振成像技术。我们的假设是，这是可以实现的 使用多参数非对比MRI序列，结合最新的高信噪比(SNR) 颈部特定形状(NSS)射频线圈和创新的机器学习(深度神经网络)分析方法。 目标1：我们将在我们的5个参与中心中的每个中心安装相同的RF线圈、MRI序列和方案 以及严格测试所有中心的测量精度和图像质量的再现性。目标 2：我们将开发、培训和验证一个用户友好的、深度学习的神经网络系统 对易损动脉粥样硬化斑块中存在的几个关键成分进行分析。目标3： 我们将把这项分析应用于一组颈动脉疾病受试者，以确定 定量测量，以及与组织病理学相比较的定性的准确性。虽然 我们将在一个高技能的学术网络中开发和测试图像质量、重复性和可靠性 我们将把这些方法设计成适用于社区医院的环境。在……结束时 在这个项目中，我们建议有一个可用于后续调查的综合解决方案，例如： 药物干预改变斑块成分的作用；研究斑块的演变 随着时间的推移，未经治疗的动脉粥样硬化性疾病的特征；并最终调查 对有害结果的预测，这可用于改进该人群的干预策略。 在成功完成后，射频线圈和核磁共振序列和分析方法将可用于 其他成像中心的方式最终改变了诊断和管理 颈动脉粥样硬化性疾病的治疗。

Abstract: Numerous investigations over the past decades have yielded substantial innovations in MR methods for the characterization of extracranial carotid atherosclerosis. Images obtained with these innovations under ideal conditions have given clinicians rich information about disease in the arterial wall and the hope for tools critically needed for adequate management of this insidious disease. Despite this, the great potential power of this technology has not made it into the routine clinical armamentarium. Indeed, because of the need for gadolinium-based contrast agents (GBCA), the long exam time (typically about 45 minutes to obtain the multiple contrasts in the 5 or 6 necessary sequences), and the steep learning curve required to interpret multi- contrast MRI most practitioners still revert to the simplified metric of diameter stenosis in assessing risk. After many collective years of investigations, the consortium of investigators collaborating on this proposal believes that the time is right to address these remaining limitations and ultimately shift the clinical paradigm. Overarching hypothesis: To achieve the great potential in the management of cervical carotid disease, a highly efficient and easily used MRI technique is required. Our hypothesis is that this can be accomplished using multi-parametric non-contrast MRI sequences coupled with the latest high signal to noise ratio (SNR) neck-shape-specific (NSS) RF coils and innovative machine learning (deep neural network) analysis methods. Aim 1: We will install identical RF coils, MRI sequences, and protocols at each of our 5 participating centers as well as rigorously test the accuracy of measurements and reproducibility of image quality from all centers. Aim 2: We will develop, train, and validate a user friendly, deep learning neural network system for the quantitative analysis of several key components considered to be present in the vulnerable atherosclerotic plaque. Aim 3: We will apply the analysis to a cohort of carotid disease subjects to establish the repeatability of the quantitative measures, as well as the accuracy of characterization in comparison to histopathology. Although we will develop and test the image quality, reproducibility and reliability in a network of highly skilled academic centers, we will design these methods to be applicable in the community hospital setting. At the conclusion of this project, we propose to have an integrated solution that can be used in subsequent investigations such as: the effect of pharmacologic intervention in modifying the composition of the plaque; studying the evolution of features of the untreated atheromatous disease over time; and, eventually, investigating the metrics that are predictive of deleterious outcomes, and that can be used in improving intervention strategies in this population. On successful completion, the RF coils and MRI sequences and analysis methods will be made available to other imaging centers in a manner that ultimately changes the paradigm of diagnosis and managing the treatment of cervical carotid atherosclerotic disease.