Functional Cardiovascular 4D MRI in Congenital Heart Disease

先天性心脏病中的功能性心血管 4D MRI

• 批准号: 10117035
• 负责人: Michael Markl
• 金额: $ 70.98万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10117035
• 关键词: 3-Dimensional; 4D MRI; Acceleration; Address; Adult; Affect; Age; Anatomy; Aortic Diseases; Archives; Atlases; Biological Markers; Blood flow; Cardiac Output; Cardiovascular system; Child; Clinical; Common Ventricle; Communities; Complex; Congenital Abnormality; Consumption; Contrast Media; Coupled; Data; Data Analyses; Data Set; Databases; Diagnostic; Diagnostic tests; Dimensions; Disease Progression; Exposure to; Funding; General Anesthesia; Goals; Growth; Heart Abnormalities; Heart Rate; High Performance Computing; Histopathology; Hospitals; Image; Infant; Link; Live Birth; Longitudinal Studies; Lung; Machine Learning; Magnetic Resonance Imaging; Manuals; Measures; Methodology; Methods; Operative Surgical Procedures; Outcome; Outcome Study; Oxygen; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Physiology; Play; Population; Process; Reproducibility; Research; Resolution; Resources; Role; Secure; Sex Differences; Testing; Time; Translations; adverse outcome; analysis pipeline; aortic valve; automated analysis; base; bicuspid aortic valve; clinical translation; cluster computing; cohort; congenital heart disorder; data analysis pipeline; data archive; data complexity; data sharing; data standards; design; exercise capacity; flexibility; follow-up; hemodynamics; improved; improved outcome; in vivo; novel; novel diagnostics; outcome prediction; patient population; pediatric patients; prevent; shear stress; spatiotemporal; tissue degeneration; tool; vascular abnormality

SUMMARY / ABSTRACT Congenital heart disease (CHD) is the most common birth defect, affecting 1.2% of all live births. Imaging plays a major role in managing CHD but remains challenging for evaluating complex cardiac and vascular abnormalities across a wide range of age and habitus. To address these limitations, the PIs have developed cardiovascular 4D flow MRI which can measure complex 3D blood flow in-vivo, a task difficult or impossible to obtain with other imaging strategies. Recent efforts have focused on two forms of CHD: 1) bicuspid aortic valve (BAV) which is the most common form of CHD, and 2) single ventricle physiology (SVP), one of the most severe forms of CHD. Our 4D flow MRI studies have successfully identified new hemodynamic biomarkers to better characterize CHD. We were the first to establish a physiologic link between aberrant 3D blood flow, elevated wall shear stress (WSS), aortopathy phenotype, and aortic wall tissue degeneration on histopathology in patients with BAV. In patients with SVP, our findings demonstrated relationships between surgical correction strategies and flow distribution to the lungs, a known factor implicated in SVP outcome. We have achieved successful clinical translation at Northwestern, where 4D flow MRI is now used as a clinical tool in diagnostic MRI exams for patients with CHD and aortic disease. Over the past four years, the PIs have assembled one of the largest 4D MRI databases with over 2500 patient exams. For this renewal application, we identified a need to increase the dynamic range of 4D MRI flow sensitivity to account for data complexity (3D + time) and the wide age range in CHD by a combination of dual-venc flow encoding, compressed sensing, and SSFP imaging. Second, three is a need for longitudinal studies to identify predictors of BAV and SVP outcome. Third, making these unique but complex 4D MRI data sets and analysis tools more widely available to the greater research community is challenging. In addition, no automated methods currently exist for advanced processing such as atlas based analysis across large cohorts. Analysis is thus time consuming and requires manual interactions (e.g. 3D vessel segmentation) which limits reproducibility and translation. To address this need, an established Northwestern data archival and pipeline processing resource based on remote high-performance computing clusters (NUNDA) will be utilized for standardized data archival, sharing, and pipeline processing of 4D MRI data. This platform will provide the unique opportunity to utilize annotated data available in the 4D MRI database (>1300 BAV, SVP, and control 4D MRI data analyzed in the initial funding cycle) for application of machine learning concepts to establish (semi-)automated 4D MRI analysis workflows in NUNDA. Thus, the renewal application for this study aims to 1) develop a rapid (15 min) non-contrast 4D MRI for clinical translation, 2) leverage the existing large 4D MRI database to identify 4D MRI metrics predictive of long-term (> 5 years) CHD patient outcome, and 3) establish a remote NUNDA platform for 4D MRI data sharing and automated analysis across large cohorts.

总结/摘要 先天性心脏病（CHD）是最常见的出生缺陷，影响所有活产婴儿的1.2%。成像 在治疗CHD中起着重要作用，但在评估复杂的心脏和血管疾病方面仍然具有挑战性 各种年龄和体质的异常。为了解决这些局限性，PI开发了 心血管4D血流MRI可以测量体内复杂的3D血流，这是一项很难或不可能完成的任务， 用其他的成像方法。最近的努力集中在两种形式的冠心病：1）二叶主动脉瓣 (BAV)这是CHD最常见的形式，和2）单心室生理学（SVP），最常见的一种 严重的CHD。我们的4D Flow MRI研究已成功确定了新的血流动力学生物标志物， 更好地表征CHD。我们是第一个建立异常三维血流， 组织病理学显示壁切应力（WSS）升高、动脉病变表型和主动脉壁组织变性 在BAV患者中。在SVP患者中，我们的研究结果表明手术矫正与 策略和肺的流量分布，这是SVP结果中涉及的已知因素。我们已经取得 在西北大学成功的临床翻译，4D Flow MRI现在被用作诊断的临床工具， 冠心病和主动脉疾病患者的MRI检查。在过去的四年里，PI已经组装了一个 最大的4D MRI数据库，包含2500多个患者检查。 对于该更新申请，我们确定需要增加4D MRI血流灵敏度的动态范围， 考虑到数据复杂性（3D+时间）和CHD的广泛年龄范围， 编码、压缩传感和SSFP成像。第二，三是需要进行纵向研究， BAV和SVP结局的预测因素。第三，使这些独特但复杂的4D MRI数据集和分析 更广泛地向更大的研究界提供工具是一项挑战。此外，没有自动化 目前存在用于高级处理的方法，例如跨大群组的基于图谱的分析。分析是 因此耗时并且需要手动交互（例如，3D血管分割），这限制了 再现性和翻译。为了满足这一需求，建立了西北数据档案和管道 基于远程高性能计算集群（NUNDA）的处理资源将用于 4D MRI数据的标准化数据存档、共享和流水线处理。该平台将提供 利用4D MRI数据库中可用的注释数据的独特机会（> 1300 BAV、SVP和对照 4D MRI数据在初始资金周期中进行分析），以应用机器学习概念来建立 NUNDA中的（半）自动化4D MRI分析工作流程。因此，本研究的更新申请旨在 1)开发用于临床翻译的快速（15分钟）非造影4D MRI，2）利用现有的大型4D MRI 数据库，以确定预测长期（> 5年）CHD患者结局的4D MRI指标，以及3）建立 远程NUNDA平台，用于大型队列的4D MRI数据共享和自动分析。

项目成果

Valve-Related Hemodynamics Mediate Human Bicuspid Aortopathy: Insights From Wall Shear Stress Mapping.

• DOI: 10.1016/j.jacc.2015.06.1310
• 发表时间: 2015-08-25
• 期刊: Journal of the American College of Cardiology
• 影响因子: 24
• 作者: Guzzardi DG;Barker AJ;van Ooij P;Malaisrie SC;Puthumana JJ;Belke DD;Mewhort HE;Svystonyuk DA;Kang S;Verma S;Collins J;Carr J;Bonow RO;Markl M;Thomas JD;McCarthy PM;Fedak PW
• 通讯作者: Fedak PW

Improved Semiautomated 4D Flow MRI Analysis in the Aorta in Patients With Congenital Aortic Valve Anomalies Versus Tricuspid Aortic Valves.

• DOI: 10.1097/rct.0000000000000312
• 发表时间: 2016-01
• 期刊: Journal of computer assisted tomography
• 影响因子: 1.3
• 作者: Schnell S;Entezari P;Mahadewia RJ;Malaisrie SC;McCarthy PM;Collins JD;Carr J;Markl M
• 通讯作者: Markl M

Aortic relative pressure components derived from four-dimensional flow cardiovascular magnetic resonance.

• DOI: 10.1002/mrm.25015
• 发表时间: 2014-10
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Lamata, Pablo;Pitcher, Alex;Krittian, Sebastian;Nordsletten, David;Bissell, Malenka M.;Cassar, Thomas;Barker, Alex J.;Markl, Michael;Neubauer, Stefan;Smith, Nicolas P.
• 通讯作者: Smith, Nicolas P.

Comparison of 4D flow and 2D velocity-encoded phase contrast MRI sequences for the evaluation of aortic hemodynamics.

• DOI: 10.1007/s10554-016-0938-5
• 发表时间: 2016-10
• 期刊: INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
• 影响因子: 2.1
• 作者: Bollache, Emilie;van Ooij, Pim;Powell, Alex;Carr, James;Markl, Michael;Barker, Alex J.
• 通讯作者: Barker, Alex J.

k-t accelerated aortic 4D flow MRI in under two minutes: Feasibility and impact of resolution, k-space sampling patterns, and respiratory navigator gating on hemodynamic measurements.

• DOI: 10.1002/mrm.26661
• 发表时间: 2018-01
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Bollache E;Barker AJ;Dolan RS;Carr JC;van Ooij P;Ahmadian R;Powell A;Collins JD;Geiger J;Markl M
• 通讯作者: Markl M