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

颈动脉粥样硬化疾病高效磁共振成像和分析的多中心实施和验证

• 批准号: 10684192
• 负责人: Maria I. Altbach
• 金额: $ 120.63万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684192
• 关键词: 3-Dimensional; Academic skills; Address; Arterial Fatty Streak; Atherosclerosis; Carotid Arteries; Carotid Artery Plaques; Carotid Atherosclerotic Disease; Cephalic; Cervical; Classification; Clinical; Collaborations; Community Hospitals; Computer software; Contrast Media; Coupled; Data; Diagnosis; Diameter; Disease; Equipment; Evolution; Gadolinium; Goals; Histology; Histopathology; Image; Intervention; Investigation; Learning; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Neck; Noise; Outcome; Patients; Physiologic pulse; Population; Protocols documentation; RF coil; Reading; Reporting; Reproducibility; Research Personnel; Risk; Risk Assessment; Shapes; Signal Transduction; Stenosis; System; Techniques; Technology; Testing; Time; Training; Validation; Vendor; clinical practice; cohort; deep neural network; design; experience; imaging facilities; imaging modality; improved; innovation; machine learning classification; patient population; pharmacologic; skills; stroke risk; success; tool; user-friendly

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.

抽象的： 在过去的几十年中，许多调查在MR方法中产生了大量创新 颅外动脉粥样硬化的表征。在理想下使用这些创新获得的图像 条件为临床医生提供了有关动脉壁上疾病的丰富信息和工具的希望 需要对这种阴险疾病的充分管理至关重要。尽管如此， 这项技术尚未进入常规的临床武术。确实，因为需要 基于Gadolinium的对比剂（GBCA），长期考试（通常约45分钟以获得 在5或6个必要序列中的多个对比度），以及解释多种的陡峭学习曲线 对比MRI，大多数从业者仍将直径狭窄的简化度量恢复为评估风险。后 许多集体调查，调查人员的联盟合作就此提案认为 解决这些剩余局限性并最终改变临床范式的时候是正确的。 总体假设：为颈颈疾病的管理中的巨大潜力 需要高效且易于使用的MRI技术。我们的假设是可以实现 使用多参数非对比度MRI序列，并结合最新的高信号与噪声比（SNR） 颈形特异性（NSS）RF线圈和创新的机器学习（深神经网络）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中安装相同的RF线圈，MRI序列和协议 以及严格测试所有中心的测量准确性和图像质量的可重复性。目的 2：我们将开发，培训和验证用户友好，深度学习的神经网络系统的定量 分析被认为存在于脆弱的动脉粥样硬化斑块中的几个关键成分。目标3： 我们将将分析应用于颈动脉疾病受试者的队列，以建立 与组织病理学相比，定量测量以及表征的准确性。虽然 我们将在高技能学术的网络中开发和测试图像质量，可重复性和可靠性 中心，我们将设计这些方法适用于社区医院环境。结束 该项目，我们建议拥有一个集成解决方案，该解决方案可用于随后的研究，例如： 药理干预在修饰斑块组成方面的影响；研究的演变 随着时间的流逝，未治疗的动脉瘤疾病的特征；最终，调查了 预测有害结果，并且可以用于改善该人群的干预策略。 成功完成后，RF线圈和MRI序列和分析方法将提供给 其他成像中心以最终改变诊断范式的方式 颈颈动脉粥样硬化疾病的治疗。