Innovative MRI-based Characterization of Cardiac Dyssynchrony

基于 MRI 的创新心脏不同步表征

• 批准号: 8875394
• 负责人: Leon Axel
• 金额: $ 63.43万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8875394
• 关键词: Apical; Cardiac; Cardiovascular system; Clinical; Clinical Markers; Clinical Research; Complex; Data; Databases; Disease; Echocardiography; Electrocardiogram; Goals; Guidelines; Heart; Heart Diseases; Heart failure; Image; Imaging Techniques; Individual; Lead; Left ventricular structure; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Measures; Mechanics; Methodology; Methods; Modeling; Motion; Movement; Outcome; Output; Patients; Pattern; Research; Resolution; Selection Criteria; Shapes; Solutions; Structure; Techniques; Technology; Testing; Time; Treatment outcome; base; biomechanical model; cardiovascular visualization; effective therapy; heart motion; improved; innovation; novel; predictive marker; public health relevance; response; spatiotemporal; success; therapy outcome; tool

 DESCRIPTION (provided by applicant): Cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. The goal of this proposal is to develop and provide a new analysis technique for understanding the complex cardiac motion patterns (in time and space) of patients with cardiac dyssynchrony, with the hope of improving its treatment outcomes, specifically with respect to cardiac resynchronization therapy (CRT). CRT, the most effective treatment for dyssynchrony with worsening heart failure, significantly improves outcome in only ~66% of heart failure patients selected for the treatment, which is based on ECG criteria. Selection criteria based on imaging are essential to improving the success rate. Unfortunately, response rates are not higher with selection criteria based on echocardiography, the most popular cardiac imaging technique. Cine cardiovascular magnetic resonance (CMR) has the potential to better characterize dyssynchrony, as it shows cardiac mechanics and intramural wall motion with much higher spatial resolution than echocardiography. However, quantitative assessments of CMR have been mostly limited to global volumetric measures, which ignore most of the motion information captured by the images. For example, studies of a number of distinctive motion features of dyssynchrony (such as septal flash and apical rocking) have been confined to qualitative assessment, limiting inference of their potential utility for improving CRT treatment. To accurately quantify cardiac function through CMR, we have developed biomechanical models for describing cardiac function and machine learning technology for identifying morphological and functional patterns atypical for healthy hearts. We propose to combine these two technologies to accurately quantify cardiac dyssynchrony within the Left Ventricle (LV). Specifically, our methods will extract a rich description of LV motion and strain from the CMRs of a set of retrospectively selected subjects with synchronous or dyssynchronous LV motion. We will then use machine learning methods to identify local and global motion patterns specific to dyssynchrony. Finally, we will correlate these patterns to already existing clinical scores to find potentially predictive markers with respect to CRT outcome. We hypothesize that these markers will have a higher correlation to CRT outcome than current clinical markers alone. Identifying these markers will have the potential to further stratify the disease with respect to the expected outcome of CRT, which then can be used to derive new selection criteria that lead to higher success rates. The project will also disseminate our novel, data-driven methodology for quantifying that motion. Other research groups can apply our tools to specifically study dyssynchrony, as well as other cardiac diseases impacting LV motion in general.

 描述（由适用提供）：心脏异位障碍检测心脏功能，通常无法有效治疗。该提案的目的是开发并提供一种新的分析技术，以了解心脏不良症患者的复杂心脏运动模式（在时空和空间中），以期改善其治疗结果，特别是在心脏重新同步治疗（CRT）方面。 CRT是对遗憾心力衰竭的最有效治疗的治疗方法，仅在选择接受治疗的心力衰竭患者中，仅66％的心力衰竭患者（基于ECG标准）可显着改善预后。基于成像的选择标准对于提高成功率至关重要。不幸的是，基于超声心动图（最流行的心脏成像技术）的选择标准的响应率并不高。 Cine心血管磁共振（CMR）具有更好地表征异性障碍的潜力，因为它显示了心脏力学和壁内壁运动，其空间分辨率比超声心动图更高。但是，CMR的定量评估主要限于全球容积量度，这些措施忽略了图像捕获的大多数运动信息。例如，对许多独特的运动特征的研究（例如闪光灯和根尖摇摆）仅限于定性评估，这限制了其对改善CRT治疗的潜在效用的推断。为了通过CMR准确量化心脏功能，我们开发了用于描述心脏功能和机器学习技术的生物力学模型，以识别健康心脏非典型的形态和功能模式。我们建议将这两种技术相结合，以准确量化左心室（LV）内的心脏异常障碍。具体而言，我们的方法将提取有关LV运动的丰富描述， 来自一组带有同步或同步LV运动的回顾性选择受试者的CMR应变。然后，我们将使用机器学习方法来识别特定于障碍的本地和全球运动模式。最后，我们将将这些模式与已经存在的临床分数相关联，以找到有关CRT结果的潜在预测标记。我们假设这些标记与CRT结果的相关性将比仅当前临床标记更高。确定这些标记将有可能进一步分类CRT预期结果的疾病，然后可用于得出新的选择标准，从而导致更高的成功率。该项目还将传播我们的新颖，数据驱动的方法来量化该运动。其他研究小组可以将我们的工具应用于特殊研究失调症，以及总体上影响LV运动的其他心脏病。