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: 10280858
• 负责人: Maria I. Altbach
• 金额: $ 133.11万
• 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/10280858
• 关键词: 3-Dimensional; Address; Arterial Fatty Streak; Atherosclerosis; Caliber; Carotid Arteries; Carotid Artery Plaques; Carotid Atherosclerotic Disease; Cervical; Classification; Clinical; Collaborations; Community Hospitals; Computer software; Contrast Media; Coupled; Data; Diagnosis; Disease; Equipment; Evolution; Gadolinium; Goals; Histology; Histopathology; Image; Intervention; Investigation; Learning; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Neck; Noise; Outcome; Pathway Analysis; Patients; Pharmacology; Physiologic pulse; Population; Protocols documentation; RF coil; Reading; Reporting; Reproducibility; Research Personnel; Risk; Sequence Analysis; Shapes; Signal Transduction; Stenosis; System; Techniques; Technology; Testing; Time; Training; Validation; Vendor; base; clinical practice; cohort; deep neural network; design; experience; imaging modality; improved; innovation; patient population; 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： 过去几十年的大量研究在 MR 方法方面取得了实质性创新 颅外颈动脉粥样硬化的特征。在理想情况下通过这些创新获得的图像 这些条件为临床医生提供了有关动脉壁疾病的丰富信息，并为开发工具带来了希望 充分管理这种潜伏疾病迫切需要。尽管如此，其巨大的潜在力量 该技术尚未进入常规临床设备。确实，因为需要 钆基造影剂 (GBCA)，检查时间较长（通常约 45 分钟才能获得 5或6个必要序列中的多重对比），以及解释多重所需的陡峭学习曲线 对比 MRI 大多数从业者在评估风险时仍然使用直径狭窄的简化指标。后 经过多年的集体调查，合作研究该提案的调查人员联盟认为 现在是解决这些剩余局限性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， 需要高效且易于使用的 MRI 技术。我们的假设是这可以实现 使用多参数非对比 MRI 序列以及最新的高信噪比 (SNR) 颈部形状特定 (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 5 个参与中心中的每一个中心安装相同的射频线圈、MRI 序列和协议，如下所示： 并严格测试所有中心的测量准确性和图像质量的再现性。目的 2：我们将开发、训练和验证用户友好的深度学习神经网络系统，用于定量分析 分析被认为存在于易损动脉粥样硬化斑块中的几个关键成分。目标 3： 我们将把该分析应用于一组颈动脉疾病受试者，以确定该研究的可重复性 定量测量，以及与组织病理学相比表征的准确性。虽然 我们将在高技能学术网络中开发和测试图像质量、再现性和可靠性 中心，我们将设计这些方法以适用于社区医院环境。结束时 在这个项目中，我们建议有一个集成的解决方案，可用于后续调查，例如： 药物干预对改变斑块成分的影响；研究进化 未经治疗的动脉粥样硬化疾病随时间变化的特征；并最终调查指标 预测有害结果，可用于改善该人群的干预策略。 成功完成后，射频线圈、MRI 序列和分析方法将可供 其他影像中心的方式最终改变了诊断和管理的范式 治疗颈动脉粥样硬化性疾病。